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The creation of photographic archives that specialised in reproductions of art and architecture began soon after the announcements in early 1839 of the first photographic processes, the daguerreotype and the photogenic drawing. These resources were built by public institutions and by private individuals. The format of photographic print storage types evolved from portfolios to book-form Solander boxes. From the 1980s on, analogue electronic and digital technologies began to offer potential solutions to the creation of digital photographic archives. Text-based projects initially began the journey. European Commission sponsored research projects and other initiatives, built the momentum. The technological capabilities of digital input, storage, and display devices and the associated logistical and financial barriers were progressively addressed, and in 2024 rich digital image resources for the teaching and research of art and architectural history now exists. Die Einrichtung von Fotoarchiven, die sich auf die Reproduktion von Kunst und Architektur spezialisierten, begann bald nach Ankündigung der ersten fotografischen Verfahren –Daguerreotypie und fotogenische Zeichnung beziehungsweise Salzdruck – zu Beginn des Jahres 1839. Diese Bestände wurden von öffentlichen Institutionen und von Privatpersonen aufgebaut. Das Format der Aufbewahrungsmöglichkeiten fotografischer Abzüge entwickelte sich von Mappen hin zu buchförmigen Solander-Boxen. Ab den 1980er Jahren boten analoge elektronische und digitale Technologien allmählich mögliche Lösungen zur Schaffung digitaler Fotoarchive. Textbasierte Vorhaben standen damals am Anfang des Prozesses. Von der Europäischen Kommission geförderte Forschungsprojekte und andere Initiativen gaben den Anstoß. Die technologischen Möglichkeiten von digitalen Eingabe-, Speicher- und Wiedergabegeräten und die damit verbundenen logistischen und finanziellen Herausforderungen wurden nach und nach angegangen, und 2024 stehen schließlich umfangreiche digitale Bildressourcen für Lehre und Forschung im Bereich Kunst- und Architekturgeschichte zur Verfügung [ABSTRACT FROM AUTHOR]

ABSTRACT Background Aims Methods Results Conclusions Recent advancements in artificial intelligence (AI) have significantly impacted dermatology, particularly in diagnosing skin diseases. However, aesthetic dermatology faces unique challenges due to subjective evaluations and the lack of standardized assessment methods.This review aims to explore the current state of AI in dermatology, evaluate its application in diagnosing skin conditions, and discuss the limitations of traditional evaluation methods in aesthetic dermatology. Additionally, the review proposes strategies for future integration of AI to address existing challenges.A comprehensive review of AI applications in dermatology was conducted, in both diagnostic and aesthetic fields. Traditional methods such as subjective surveys and hardware devices were analyzed and compared with emerging AI technologies. The limitations of current AI models were evaluated, and the need for standardized evaluation methods and diverse datasets was identified.AI has shown great potential in diagnosing skin diseases, particularly skin cancer. However, in aesthetic dermatology, traditional methods remain subjective and lack standardization, therefore limiting their effectiveness. Emerging AI applications in this field show promise, but they have significant limitations due to biased datasets and inconsistent evaluation methods.To develop the potential of AI in aesthetic dermatology, it is crucial to create standardized evaluation methods, collect diverse datasets reflecting various ethnicities and ages, and educate practitioners on AI's utility and limitations. Addressing these challenges will improve diagnostic accuracy, better patient outcomes, and help integrate AI effectively into clinical practice. [ABSTRACT FROM AUTHOR]

Intraoperative frozen section (IFS) is used with the intention to improve functional and oncological outcomes for patients undergoing radical prostatectomy (RP). High resource requirements of IFS techniques such as NeuroSAFE may preclude widespread adoption, even if there are benefits to patients. Recent advances in fresh‐tissue microscopic digital imaging technologies may offer an attractive alternative, and there is a growing body of evidence regarding these technologies. In this narrative review, we discuss some of the familiar limitations of IFS and compare these to the attractive counterpoints of modern digital imaging technologies such as the speed and ease of image generation, the locality of equipment within (or near) the operating room, the ability to maintain tissue integrity, and digital transfer of images. Confocal laser microscopy (CLM) is the modality most frequently reported in the literature for margin assessment during RP. We discuss several imitations and obstacles to widespread dissemination of digital imaging technologies. Among these, we consider how the 'en‐face' margin perspective will challenge urologists and pathologists to understand afresh the meaning of positive margin significance. As a part of this, discussions on how to describe, categorize, react to, and evaluate these technologies are needed to improve patient outcomes. Limitations of this review include its narrative structure and that the evidence base in this field is relatively immature but developing at pace. [ABSTRACT FROM AUTHOR]

A novel colorimetric approach specifically designed to effectively identify the presence of 3-aminophenol (3-AP) in environmental water is introduced. Briefly, a nitrogen-doped carbon-supported cobalt nanozyme (Co@CN-1) was synthesized and utilized to improve the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) in the presence of peroxymonosulfate (PMS). Comparative catalytic reactions confirmed that the performance of PMS as an activator exceeds that of hydrogen peroxide catalytically by a factor of 3.5. The catalytic reaction parameters underwent optimization, further resulting in the derivation of a linear detection equation for 3-AP, expressed as inhibition rate (IR%) = 3.35[3-AP]—4.36 (0–20 μM, R2 = 0.994) and IR% = 1.43[3-AP] + 31.87 (20–36 μM, R2 = 0.992), with the limit of detection (LOD) of 2.84 μM. The linear relationship between 3-AP concentration and the conversion of color to grayscale value (GSV) was established by smartphones, expressed as GSV = 1.28[3-AP] + 147.10 (R2 = 0.972). Density functional theory calculations revealed that Co acts as the preferred active site for donating electrons in PMS activation. This work provides a rapid and accurate approach for monitoring 3-AP concentration, enabling real-time analysis and potentially contributing to environmental and ecological studies. [ABSTRACT FROM AUTHOR]

Background/Objectives: Locating the mandibular foramen (MF) through imaging is clinically important for inferior alveolar nerve (IAN) anesthesia and mandibular ramus osteotomies. Although cone-beam computed tomography (CBCT) is superior in imaging the mandible, an orthopantomogram (OPG) is preferred for its ease of use and availability. Therefore, the present study aimed to evaluate the accuracy of digital OPG in localizing the MF, in a subset of the Middle Eastern population. Methods: Radiographic images (OPG and CBCT) of selected patients (adults, dentulous and no mandibular abnormalities) were used to locate the MF through digital measurements (mm) of the anteroposterior distance from the anterior border of the ramus (MF-AP) and the superoinferior position from the mandibular occlusal plane (MF-SI). Measurements were statistically compared between OPG and CBCT for accuracy. Differences in measurements between OPG and CBCT were compared against the anatomic location (right/left), age and biological sex, assuming a p-value < 0.05 as significant. Results: A total of 204 radiographic records (males: 100/females: 104/mean age: 34.65 ± 11.55 years) were evaluated. The measurements for the MF were MF-AP-OPG (right: 13.53 ± 2.44/left: 13.19 ± 2.25), MF-AP-CBCT (right: 13.61 ± 2.39/left: 13.36 ± 2.19), MF-SI-OPG (right: 5.25 ± 1.71/left: 5.41 ± 1.65) and MF-SI-CBCT (right: 5.59 ± 1.66/left: 5.52 ± 1.61). Measurements between OPG and CBCT were not significantly different, except for MF-SI (right) (p = 0.042). While the overall difference between OPG and CBCT (MF-AP/MF-SI) measurements showed a significant association (p < 0.01) with the anatomic location (right/left), a significant association (p < 0.05) with biological sex was observed only for MF-AP. Conclusions: Based on this study's outcomes, digital OPG is an accurate modality to locate the MF based on anteroposterior (MF-AP) and superoinferior (MF-SI) measurements. This would be clinically beneficial for dental and oral surgeons to achieve the optimum IAN block anesthesia based on preoperative panoramic radiographs. Similarly, it would assist maxillofacial surgeons in planning mandibular orthognathic surgeries and ramus osteotomies without complications. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the diagnostic capability of 5th‐year students using digital imaging, conventional bitewing (BW) radiographs, and printed film on paper to detect interproximal caries lesions. Methods: A cross‐sectional study was conducted with senior dental students. Three different radiographs: digital, BW, and printed films on paper were used; thus, nine radiographs, each with a 2‐min viewing time, were considered by students along with a questionnaire. A control group of specialists from Prosthodontics and Radiology had finalized the answers prior to conducting the study. The appropriate responses were divided into five categories: R0: Intact surface, R1: Radiolucency in the outer half of enamel, R2: Radiolucency in the inner half of enamel, R3: Radiolucency in the outer half of dentin, and R4: Radiolucency in the inner half of dentin. Students' responses were analyzed using a one‐way analysis of variance (ANOVA) test and a t‐test. Results: Ethics for the study was obtained from the institutional committee (Reg No: BM19/9/8). When compared with the control group using ANOVA testing, the results showed good detection accuracy with a success rate of ~64 accuracy. There was a significant difference in the outcomes when detecting the presence of the caries lesion between the three diagnostic techniques (p > 0.001). In detecting the size of the carious lesion, the students' ability was recorded as poor. The highest average for detecting the presence of the carious lesion was correspondent to the printed film on paper method. Conclusions: Senior dental students have shown good accuracy in detecting the presence but not the size of interproximal caries on all radiographs viewed. Clinical Significance: Teachings and availability of diverse radiological diagnostic techniques ensured a reasonable level of understanding and use of the diagnostic caries risk assessment methodologies as required in restorative treatment planning. [ABSTRACT FROM AUTHOR]

Graffiti, by their very nature, are ephemeral, sometimes even vanishing before creators finish them. This transience is part of graffiti's allure yet signifies the continuous loss of this often disputed form of cultural heritage. To counteract this, graffiti documentation efforts have steadily increased over the past decade. One of the primary challenges in any documentation endeavour is identifying and recording new creations. Image‐based change detection can greatly help in this process, effectuating more comprehensive documentation, less biased digital safeguarding and improved understanding of graffiti. This paper introduces a novel and largely automated image‐based graffiti change detection method. The methodology uses an incremental structure‐from‐motion approach and synthetic cameras to generate co‐registered graffiti images from different areas. These synthetic images are fed into a hybrid change detection pipeline combining a new pixel‐based change detection method with a feature‐based one. The approach was tested on a large and publicly available reference dataset captured along the Donaukanal (Eng. Danube Canal), one of Vienna's graffiti hotspots. With a precision of 87% and a recall of 77%, the results reveal that the proposed change detection workflow can indicate newly added graffiti in a monitored graffiti‐scape, thus supporting a more comprehensive graffiti documentation. [ABSTRACT FROM AUTHOR]

The concept of zoom in scanning electron microscopy (SEM) once referred to increasing the magnification of the sample by reducing the scanned field size. However, with the introduction of digital technology, the concept of zooming has become a little more complicated. Historically, older SEMs operated at a 512 × 512-pixel lateral resolution (262 kB pixel density) at TV scan rates. Today's newest SEMs can achieve up to 32k × 24k (4:3 ratio) pixel lateral resolution, equating to an 805 MB pixel density. In this article, we explore the factors involved in selecting an appropriate frame grabber size for SEM imaging. Specifically, we will delve into the implications of pixel density, including its impact on digital zoom capabilities, image file sizes, and overall image acquisition times. [ABSTRACT FROM AUTHOR]

In order to detect rice false smut quickly and accurately, a lightweight false smut detection model, YOLOv8n-MBS, was proposed in this study. The model introduces the C2f_MSEC module to replace C2f in the backbone network for better extraction of key features of false smut, enhances the feature fusion capability of the neck network for different sizes of false smut by using a weighted bidirectional feature pyramid network, and designs a group-normalized shared convolution lightweight detection head to reduce the number of parameters in the model head to achieve model lightweight. The experimental results show that YOLOv8n-MBS has an average accuracy of 93.9%, a parameter count of 1.4 M, and a model size of 3.3 MB. Compared with the SSD model, the average accuracy of the model in this study increased by 4%, the number of parameters decreased by 89.8%, and the model size decreased by 86.9%; compared with the YOLO series of YOLOv7-tiny, YOLOv5n, YOLOv5s, and YOLOv8n models, the YOLOv8n-MBS model showed outstanding performance in terms of model accuracy and model performance detection; compared to the latest YOLOv9t and YOLOv10n models, the average model accuracy increased by 2.8% and 2.2%, the number of model parameters decreased by 30% and 39.1%, and the model size decreased by 29.8% and 43.1%, respectively. This method enables more accurate and lighter-weight detection of false smut, which provides the basis for intelligent management of rice blast disease in the field and thus promotes food security. [ABSTRACT FROM AUTHOR]

Objectives: In orthognatic surgery, one of the primary determinants for reliable three-dimensional virtual surgery planning (3D VSP) and an accurate transfer of 3D VSP to the patient in the operation room is the condylar seating. Incorrectly seated condyles would primarily affect the accuracy of maxillary-first bimaxillary osteotomies as the maxillary repositioning is dependent on the positioning of the mandible in the cone-beam computed tomography (CBCT) scan. This study aimed to develop and validate a novel tool by utilizing a deep learning algorithm that automatically evaluates the condylar seating based on CBCT images as a proof of concept. Materials and methods: As a reference, 60 CBCT scans (120 condyles) were labeled. The automatic assessment of condylar seating included three main parts: segmentation module, ray-casting, and feed-forward neural network (FFNN). The AI-based algorithm was trained and tested using fivefold cross validation. The method’s performance was evaluated by comparing the labeled ground truth with the model predictions on the validation dataset. Results: The model achieved an accuracy of 0.80, positive predictive value of 0.61, negative predictive value of 0.9 and F1-score of 0.71. The sensitivity and specificity of the model was 0.86 and 0.78, respectively. The mean AUC over all folds was 0.87. Conclusion: The innovative integration of multi-step segmentation, ray-casting and a FFNN demonstrated to be a viable approach for automating condylar seating assessment and have obtained encouraging results. Clinical relevance: Automated condylar seating assessment using deep learning may improve orthognathic surgery, preventing errors and enhancing patient outcomes in maxillary-first bimaxillary osteotomies. [ABSTRACT FROM AUTHOR]

Objectives: Diagnostic imaging is crucial for implant dentistry. This review provides an up‐to‐date perspective on the application of digital diagnostic imaging in implant dentistry. Methods: Electronic searches were conducted in PubMed focusing on the question 'when (and why) do we need diagnostic imaging in implant dentistry?' The search results were summarised to identify different applications of digital diagnostic imaging in implant dentistry. Results: The most used imaging modalities in implant dentistry include intraoral periapical radiographs, panoramic views and cone beam computed tomography (CBCT). These are dependent on acquisition standardisation to optimise image quality. Particularly for CBCT, other technical parameters (i.e., tube current, tube voltage, field‐of‐view, voxel size) are relevant minimising the occurrence of artefacts. There is a growing interest in digital workflows, integrating diagnostic imaging and automation. Artificial intelligence (AI) has been incorporated into these workflows and is expected to play a significant role in the future of implant dentistry. Preliminary evidence supports the use of ionising‐radiation‐free imaging modalities (e.g., MRI and ultrasound) that can add value in terms of soft tissue visualisation. Conclusions: Digital diagnostic imaging is the sine qua non in implant dentistry. Image acquisition protocols must be tailored to the patient's needs and clinical indication, considering the trade‐off between radiation exposure and needed information. growing evidence supporting the benefits of digital workflows, from planning to execution, and the future of implant dentistry will likely involve a synergy between human expertise and AI‐driven intelligence. Transiting into ionising‐radiation‐free imaging modalities is feasible, but these must be further developed before clinical implementation. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this prospective study was to determine the inter‐ and intraindividual variability in virtual single‐tooth implant positioning based on the level of expertise, specialty, total time spent, and the use of a prosthetic tooth setup. Materials and Methods: Virtual implant planning was performed on matched pre‐ and post‐extraction intraoral scans (IOS), and cone‐beam computed tomography scans of 15 patients. Twelve individual examiners, involving six novices and experts from oral surgery and prosthodontics positioned the implants, first based on anatomical landmarks utilizing only the post‐extraction, and second with the use of the pre‐extraction IOS as a setup. The time for implant positioning was recorded. After 1 month, all virtual plannings were performed again. The individual implant positions were superimposed to obtain 3D deviations using a software algorithm. Results: An interindividual variability with mean angular, crestal, and apical positional deviations of 3.8 ± 1.94°, 1.11 ± 0.55, and 1.54 ± 0.66 mm, respectively, was found. When assessing intraindividual variability, deviations of 3.28 ± 1.99°, 0.78 ± 0.46, and 1.12 ± 0.61 mm, respectively, were observed. Implants planned by experts exhibited statistically lower deviations compared to those planned by novices. Longer planning times resulted in lower deviations in the experts' group but not in the novices. Oral surgeons demonstrated lower crestal, but not angular and apical deviations than prosthodontists. The use of a setup only led to minor adjustments. Conclusions: Substantial inter‐ and intraindividual variability exists during implant positioning utilizing specialized software planning. The level of expertise and the time invested influenced the deviations of the implant position during the planning sequence. [ABSTRACT FROM AUTHOR]

Wheat stands as the foremost cultivated crop in Pakistan, dominating in terms of acreage and production among cereals. The morphology of wheat grains has been a target of selection since the dawn of agriculture and remains a paramount focus in contemporary breeding efforts. This study sought to examine alterations in grain morphology using digital imaging across 64 bread wheat cultivars grown in Pakistan under both well-watered and water-limited conditions, while also exploring their link with the TaCWI gene. The analysis revealed a substantial range of variation in grain morphology encompassing more than 15fifteen descriptors among the cultivars. Historically significant wheat cultivars, including T9 and C-271, which predate 1930, exhibited the smallest grain area, while contemporary counterparts like, Pirsabak-2013 and Markaz-2019 showcased the largest grain area. Significantly, the wheat cell wall invertase gene, TaCwi-A1, displayed a strong association with grain size. The TaCwi-A1a allele was identified in 31 out of the 64 cultivars and was notably correlated with larger grain size. This research unveils a comprehensive understanding of variations in grain size and shape among historical wheat cultivars in Pakistan and emphasizes that modern Pakistani wheat cultivars tend to exhibit larger grain size. Furthermore, it underscores the necessity of evaluating the allelic influence of additional genes related to grain size and weight, paving the way for more targeted breeding efforts in the future. [ABSTRACT FROM AUTHOR]

Introduction: In the realm of computational pathology, the scarcity and restricted diversity of genitourinary (GU) tissue datasets pose significant challenges for training robust diagnostic models. This study explores the potential of Generative Adversarial Networks (GANs) to mitigate these limitations by generating high-quality synthetic images of rare or underrepresented GU tissues. We hypothesized that augmenting the training data of computational pathology models with these GAN-generated images, validated through pathologist evaluation and quantitative similarity measures, would significantly enhance model performance in tasks such as tissue classification, segmentation, and disease detection. Methods: To test this hypothesis, we employed a GAN model to produce synthetic images of eight different GU tissues. The quality of these images was rigorously assessed using a Relative Inception Score (RIS) of 1.27 ± 0.15 and a Fréchet Inception Distance (FID) that stabilized at 120, metrics that reflect the visual and statistical fidelity of the generated images to real histopathological images. Additionally, the synthetic images received an 80% approval rating from board-certified pathologists, further validating their realism and diagnostic utility. We used an alternative Spatial Heterogeneous Recurrence Quantification Analysis (SHRQA) to assess the quality of prostate tissue. This allowed us to make a comparison between original and synthetic data in the context of features, which were further validated by the pathologist's evaluation. Future work will focus on implementing a deep learning model to evaluate the performance of the augmented datasets in tasks such as tissue classification, segmentation, and disease detection. This will provide a more comprehensive understanding of the utility of GAN-generated synthetic images in enhancing computational pathology workflows. Results: This study not only confirms the feasibility of using GANs for data augmentation in medical image analysis but also highlights the critical role of synthetic data in addressing the challenges of dataset scarcity and imbalance. Conclusions: Future work will focus on refining the generative models to produce even more diverse and complex tissue representations, potentially transforming the landscape of medical diagnostics with AI-driven solutions. [ABSTRACT FROM AUTHOR]

Objectives: Bitewing radiography is considered to be of high diagnostic value in caries detection, but owing to projections, lesions may remain undetected. The novel bitewing plus (BW +) technology enables scrolling through radiographs in different directions and angles. The present study aimed at comparing BW + with other 2D and 3D imaging methods in terms of sensitivity, specificity, and user reliability. Materials and methods: Five human cadavers were used in this study. In three cadavers, natural teeth were transplanted post-mortem. BW + , two-dimensional (digital sensors, imaging plates, 2D and 3D bitewing radiographs) and 3D methods (high and low dose CBCT) were taken. Carious lesions were evaluated on 96 teeth at three positions (mesial, distal, and occlusal) and scored according to their level of demineralization by ten observers, resulting in 35,799 possible lesions across all observers and settings. For reference, µCT scans of all teeth were performed. Results: Overall, radiographic evaluations showed a high rate of false-negative diagnoses, with around 70% of lesions remaining undetected, especially enamel lesions. BW + showed the highest sensitivity for dentinal caries and had comparatively high specificity overall. Conclusions: Within the limits of the study, BW + showed great potential for added diagnostic value, especially for dentinal caries. However, the tradeoff of diagnostic benefit and radiation exposure must be considered according to each patient's age and risk. [ABSTRACT FROM AUTHOR]

Rice blast (RB) and Brown spot (BS) are economically important diseases in rice that cause greater yield losses annually. Both share the same host and produce quite similar lesions, which leads to confusion in identification by farmers. Proper identification is essential for better management of the diseases. Visual identification needs trained experts and the laboratory-based experiments using molecular techniques are costly and time-consuming even though they are accurate. This study investigated the differentiation of the lesions from these two diseases based on proximally sensed digital RGB images and derived colour indices. Digital images of lesions were acquired using a smartphone camera. Thirty-six colour indices were evaluated by k-means clustering to distinguish the diseases using three colour channel components; RGB, HSV, and La*b*. Briefly, the background of the images was masked to target the leaf spot lesion, and colour indices were derived as features from the centre region across the lesion, coinciding with the common identification practice of plant pathologists. The results revealed that 36 indices delineated both diseases with 84.3 % accuracy. However, it was also found that the accuracy was mostly governed by indices associated with the R, G and B profiles, excluding the others. G/R, NGRDI, (R + G + B)/R, VARI, (G + B)/R, R/G, Nor_r, G-R, Mean_A, and Logsig indices were identified to contribute more in distinguishing the diseases. Therefore, these RGB-based colour indices can be used to distinguish blast and brown spot diseases using the k-means algorithm. The results from this study present an alternative, and non-destructive, objective method for identifying RB and BS disease symptoms. Based on the findings, a mobile application, Blast O spot is developed to differentiate the diseases in fields.

The article discusses the utility of cutaneous thermography in assessing nerve function impairment in leprosy. Leprosy, caused by Mycobacterium leprae and Mycobacterium lepromatosis, remains a global health concern with a significant number of new cases reported annually. The study conducted on five leprosy patients found that cutaneous temperature changes correlated with areas of sensory loss, indicating the potential of thermography as a screening tool for early neural involvement in leprosy. Further research is needed to validate these findings and explore the application of thermography in detecting leprosy and assessing nerve function impairment in field settings. [Extracted from the article]

The road construction industry aims to contribute to the protection of already compromised environment. Cold mix asphalt (CMA) is a measure initiated by the road industry to protect the environment and preserve energy. Despite having additional benefits, CMA has attracted little attention due to its inferior performance. CMA's performance is enhanced using a sustainable binder bio-modifier, natural cup lump rubber (CLR) is one of them. This study evaluated the tensile properties, rutting, moisture susceptibility, and adhesion properties of CLR-modified CMA (CMA-CR). The tensile property was enhanced by 26% due to CLR modification. CMA-CR had excellent rutting resistance of less than 2 mm rut depth at 10,000 load cycles, showing 70% improvement compared with conventional CMA. Moisture susceptibility evaluation indicated that CMA-CR had tensile strength ratio (TSR) value of 104%, satisfying the minimum 80% requirement of AASHTO T283. It also retained more than 96% bitumen coating. The moisture damage resistance was improved by 12% and 10% in terms of TSR and stripping, respectively. The durability results revealed that the CMA-CR mixture prevented higher mass loss, representing 14% improvement compared with conventional CMA.

Sundas Ejaz Maqsood,1,2 Samer Hamada,1 Damian Lake,1 Artemis Matsou,1 Mohamed Elalfy1– 3 1Corneao- Plastic Unit, Queen Victoria Hospital, East Grinsted, West Sussex, UK; 2Cornea Service, Ophthalmology, Maidstone and Tunbridge Wells NHS Trust, Kent, UK; 3Cornea Department, Research Institute of Ophthalmology, Giza, EgyptCorrespondence: Mohamed Elalfy, Ophthalmology Department, Maidstone and Tunbridge Wells NHS Trust, Hermitage Lane, Maidstone, Kent, ME16 9QQ, UK, Email Mohamed.elalfy@nhs.netPurpose: To report the outcomes of using a sutureless human amniotic membrane dehydrated matrix (HAMDM) in the management of a range of ocular surface conditions utilizing a digital ocular surface disease assessment tool.Methods: Two UK NHS Trusts — Queen Victoria Hospital Foundation Trust (East Grinstead and Maidstone) and Tunbridge Wells Trust (Kent) — prospectively treated patients with ocular surface disease with sutureless HAMDM. The patient cohort was assessed for resolution of epithelial defects, ocular surface inflammation, and best-corrected visual acuity pre- and posttreatment. Measurements of ocular surface inflammation and epithelial defect size were assessed using AOS digital imaging software, a validated tool for objective grading of bulbar conjunctival redness and measurement of corneal epithelial defects.Results: A total of 47 applications of sutureless HAMDM on 46 eyes of 46 patients (25 male, 21 female, age 9– 94 years) were assessed across various etiologies for an average of 24.0± 14.1 days. Patients with limbal stem-cell deficiency (n=17), persistent epithelial defects (n=16), neurotrophic corneal disease (n=7), filamentary keratitis (n=2), corneal erosion (n=1), corneal thinning (n=1), ocular surface inflammation (n=1), and traumatic corneal laceration (n=1) were included in the study. Across all patents, 63% of eyes showed complete healing of epithelial defects and 32.6% of eyes showed partial resolution. The average rate of healing (wound closure) was 0.36 mm2 per day across the overall patient cohort, and the rate of healing in cases with complete resolution of epithelial defects was 0.41 mm2 per day. Inflammation across all four quadrants of the ocular surface remained stable. Visual acuity across the patient cohort remained stable (61%) and improved in 26% of patients (0.06± 0.51 logMAR).Conclusion: Sutureless HAMDM application can be accomplished in just a few minutes and effectively treat a range of ocular surface disease in a clinical, nonsurgical setting. The AOS imaging software offered a quantitative methodology for measuring epithelial defect size and inflammation state.Keywords: dehydrated human amniotic membrane, sutureless, ocular surface inflammation, persistent epithelial defect, digital imaging

Background: In ACL reconstruction, it is desirable to assess preoperatively whether a sufficient graft diameter can be achieved with the planned tendon graft. The present study investigated the effect of the location of the cross-sectional area (CSA) measurement of the hamstring tendons in preoperative MRI on the correlation of the CSA with the intraoperative graft diameter. In addition, we analyzed whether the measurement results of examiners with different skill levels were comparable. Methods: A total of 32 subjects undergoing a single bundle ACL reconstruction using an autologous ipsilateral quadrupled hamstring graft (STGT) were included. The CSA of the semitendinosus and gracilis tendon was determined in preoperative MRI on six defined levels by three examiners. The intraclass correlation coefficient between the measurements of these observers was determined. The correlation between the sum of the CSA of both tendons (CSA STGT) and the graft diameter was investigated. Results: The interrater reliability was excellent on most of the investigated levels. A significant correlation between CSA STGT and the graft diameter was seen on all levels. The strongest correlation was found on the level 10 mm above the joint line. Conclusions: The measurement of the CSA STGT in the preoperative MRI 10 mm above the joint line enabled a good assessment of the achievable graft diameter in ACL reconstruction, independent of the examiners' training level. [ABSTRACT FROM AUTHOR]

As personalized medicine advances, there is an escalating need for sophisticated tools to understand complex biomechanical phenomena in clinical research. Recognizing a significant gap, this study pioneers the development of patient-specific in silico models for tooth autotransplantation (TAT), setting a new standard for predictive accuracy and reliability in evaluating TAT outcomes. Development of the models relied on 6 consecutive cases of young patients (mean age 11.66 years ± 0.79), all undergoing TAT procedures. The development process involved creating detailed in silico replicas of patient oral structures, focusing on transplanting upper premolars to central incisors. These models underpinned finite element analysis simulations, testing various masticatory and traumatic scenarios. The models highlighted critical biomechanical insights. The finite element models indicated homogeneous stress distribution in control teeth, contrasted by shape-dependent stress patterns in transplanted teeth. The surface deviation in the postoperative year for the transplanted elements showed a mean deviation of 0.33 mm (±0.28), significantly higher than their contralateral counterparts at 0.05 mm (±0.04). By developing advanced patient-specific in silico models, we are ushering in a transformative era in TAT research and practice. These models are not just analytical tools; they are predictive instruments capturing patient uniqueness, including anatomical, masticatory, and tissue variables, essential for understanding biomechanical responses in TAT. This foundational work paves the way for future studies, where applying these models to larger cohorts will further validate their predictive capabilities and influence on TAT success parameters. [ABSTRACT FROM AUTHOR]

This work and manuscript focus on how 3D scanning methodologies and post-processing analyses may help us to gain a deeper investigation of cuneiform tablets beyond the written content. The dataset proposed herein is a key part of the archaeological collection preserved in the Musei Reali of Turin in Italy; these archaeological artefacts enclose further important semantic information extractable through detailed 3D documentation and 3D model filtering. In fact, this scanning process is a fundamental tool for better reading of sealing impressions beneath the cuneiform text, as well as for understanding micrometric evidence of the fingerprints of scribes. Most of the seal impressions were made before the writing (like a watermark), and thus, they are not detectable to the naked eye due to cuneiform signs above them as well as the state of preservation. In this regard, 3D scanning and post-processing analysis could help in the analysis of these nearly invisible features impressed on tablets. For this reason, this work is also based on how 3D analyses may support the identification of the unperceived and almost invisible features concealed in clay tablets. Analysis of fingerprints and the depths of the signs can tell us about the worker's strategies and the people beyond the artefacts. Three-dimensional models generated inside the Artec 3D ecosystem via Space Spider scanner and Artec Studio software were further investigated by applying specific filters and shaders. Digital light manipulation can reveal, through the dynamic displacement of light and shadows, particular details that can be deeply analysed with specific post-processing operations: for example, the MSII (multi-scale integral invariant) filter is a powerful tool exploited for revealing hidden and unperceived features such as fingerprints and sealing impressions (stratigraphically below cuneiform signs). Finally, the collected data will be handled twofold: in an open-access repository and through a common data environment (CDE) to aid in the data exchange process for project collaborators and common users. [ABSTRACT FROM AUTHOR]

The road construction industry aims to contribute to the protection of already compromised environment. Cold mix asphalt (CMA) is a measure initiated by the road industry to protect the environment and preserve energy. Despite having additional benefits, CMA has attracted little attention due to its inferior performance. CMA's performance is enhanced using a sustainable binder bio-modifier, natural cup lump rubber (CLR) is one of them. This study evaluated the tensile properties, rutting, moisture susceptibility, and adhesion properties of CLR-modified CMA (CMA-CR). The tensile property was enhanced by 26% due to CLR modification. CMA-CR had excellent rutting resistance of less than 2 mm rut depth at 10,000 load cycles, showing 70% improvement compared with conventional CMA. Moisture susceptibility evaluation indicated that CMA-CR had tensile strength ratio (TSR) value of 104%, satisfying the minimum 80% requirement of AASHTO T283. It also retained more than 96% bitumen coating. The moisture damage resistance was improved by 12% and 10% in terms of TSR and stripping, respectively. The durability results revealed that the CMA-CR mixture prevented higher mass loss, representing 14% improvement compared with conventional CMA. [ABSTRACT FROM AUTHOR]

Background: Chronic urticaria (CU) is characterized by transient wheals and angioedema, which are often not present when patients see their treating physician. Objective: The objective of this study was to evaluate the diagnostic value of smartphone photographs captured by patients prior to their first visit at an urticaria outpatient clinic. Methods: A survey regarding the quality and utility of smartphone photographs of urticarial skin lesions in patients with CU attending the outpatient clinic for the first time was conducted. Up to three random patient-selected photographs of skin lesions were evaluated by a physician. Results: Of 148 patients, 118 (79.7%) had taken photographs of their skin lesions prior to the consultation, and 75% took photographs with the intention of presenting it to their physician. The photographs were of wheals in 90% of the cases and angioedema in 8%. In total, 72% of the smartphone photographs had the skin lesion in focus, 64% had good resolution, and 48% had good lighting. Only 9% of the smartphone photographs were blurred, 10% had bad lighting, 4% had bad resolution, and 8% did not have the lesion in focus. Moreover, 86% of the smartphone photographs were found to be useful for clinical evaluation. At least one photograph of good/very good quality was presented by 86% of the patients, and 97% had at least one photograph that was useful for clinical evaluation. Conclusion: Patients with CU often take smartphone photographs of their skin lesions on their own initiative prior to their first consultation to present the photographs to their physician. These smartphone photographs are very often of good quality and suitable for clinical evaluation. [ABSTRACT FROM AUTHOR]

This paper designed and developed an online digital imaging excitation sensor for wind power gearbox wear condition monitoring based on an adaptive deep learning method. A digital imaging excitation sensing image information collection architecture for magnetic particles in lubricating oil was established to characterize the wear condition of mechanical equipment, achieving the real-time online collection of wear particles in lubricating oil. On this basis, a mechanical equipment wear condition diagnosis method based on online wear particle images is proposed, obtaining data from an engineering test platform based on a wind power gearbox. Firstly, a foreground segmentation preprocessing method based on the U-Net network can effectively eliminate the interference of bubbles and dark fields in online wear particle images, providing high-quality segmentation results for subsequent image processing, A total of 1960 wear particle images were collected in the experiment, the average intersection union ratio of the validation set is 0.9299, and the accuracy of the validation set is 0.9799. Secondly, based on the foreground segmentation preprocessing of wear particle images, by using the watered algorithm to obtain the number of particles in each size segment, we obtained the number of magnetic particle grades in three different ranges: 4–38 µm, 39–70 µm, and >70 µm. Thirdly, we proposed a method named multidimensional transformer (MTF) network. Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are used to obtain the error, and the maintenance strategy is formulated according to the predicted trend. The experimental results show that the predictive performance of our proposed model is better than that of LSTM and TCN. Finally, the online real-time monitoring system triggered three alarms, and at the same time, our offline sampling data analysis was conducted, the accuracy of online real-time monitoring alarms was verified, and the gearbox of the wind turbine was shut down for maintenance and repair. [ABSTRACT FROM AUTHOR]

Aims: To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. Methods: A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random‐effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies. Results: For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90–99.97] and 98.96 (95% CI = 98.42–99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06–99.62) overall and 96.27% (94.63–97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89–99.99)] overall and 99.93% (99.68–99.98) for bowel cancer screening samples; skin 99.99% (99.92–100.0); renal 99.99% (99.57–100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either. Conclusions: Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used. [ABSTRACT FROM AUTHOR]

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This research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field.

Mango is a highly significant fruit crop that thrives in a variety of agro-ecologies around the world. Mangoes are rich in vitamins and minerals. However, its yield is currently severely constrained due to disease and pest infestations. Thus, in order to improve mango fruit quality and productivity, illnesses and insect pests must be detected early on. In this study, we conceived and constructed a mango leaf disease detection mechanism utilizing an ensemble convolutional neural network approach. Healthy and diseased mango leaf images were manually obtained from main producing locations in Amhara Region for Merawi fruit and vegetable research identification. To improve the datasets, several pre-processing procedures (such as image resizing, noise reduction, and image augmentation) were used. To improve classification performance and meet the study's purpose, various segmentation approaches such as k means and Mask R-CNN were applied. Furthermore, following pre-processing and segmentation, features of mango leaf images were retrieved using CNN to obtain important features. The classification model was then constructed using fully-connected layer classifiers on the retrieved features of mango leaf images. The ensemble proposed GoogLeNet and VGG16 based CNN model in the study encompasses various operations, including dataset collection, image preprocessing, noise removal, segmentation, data augmentation, feature extraction, and classification. Upon testing, the model demonstrated impressive performance with 99.87 % training classification accuracy, 99.72 % validation accuracy, and 99.21 % testing accuracy. This indicates the effectiveness of the ensemble approach in achieving high accuracy in image classification tasks.

This paper explores the fusion of data science and cognitive techniques in deciphering the behaviors and emotions of farm animals. The focus is on the strategic application of digital imaging and artificial intelligence to discern subtle behavioral patterns and micro-expressions in livestock, offering a predictive window into their emotional states. The significance of acoustic vocalization analysis in interpreting complex communicative signals and emotional subtleties is highlighted. The work extends to cognitive evaluations, such as mirror tests and bias assessments, revealing higher levels of self-awareness and cognitive abilities in farm animals than previously recognized. Emphasizing the need for a synergistic approach, the paper advocates for melding technological advancements with a deep understanding of animal psychology and behavior. This ensures that technology enhances rather than supplants traditional observational methods in animal welfare. The discussion delves into various methodologies and algorithms that measure cognition, underscoring the pivotal role of cognitive computing in advancing animal welfare. A cautious and informed application of these technologies is proposed, emphasizing their role in augmenting, not undermining, the essential human-animal bond. Ultimately, this critical review calls for an ethical, empathetic, and scientifically grounded integration of cognitive computing into animal welfare practices.

The development of a portable device created by 3D printing for fluorimetric measurements is an efficient tool for analytical applications in situ or in the laboratory presenting a wide field of applications in the environmental and food field. This device uses a light-emitting diode (LED) as a radiation source and a digital microscope as a detector. Digital images obtained by the interaction between the radiation source and the sample were analyzed with the help of the YouCam software, the images were filed in JPEG format and processed with the Chemostat software.The entire system is connected to a notebook, which serves as an LED and detector power supply without the need for any additional power source. The proposed device was used for the determination in situ of quinine in water and beverage samples, respectively. For the validation of the developed system, the results obtained were compared with a conventional spectrofluorometer with a t-test at a 95% confidence level. The proposed system provides satisfactory precision and accuracy values. The proposed method presented detection and quantification limits of 0.99 and 1.46 mg·L−1, at a 95% confidence interval.

The fruit weight is an important guideline for breeders and farmers to increase marketable productions, although conventionally it requires destructive measurements. The combination of image-based phenotyping (IBP) approaches with multivariate analysis has the potential to further improve the line selection based on economical trait, like fruit weight. Therefore, this study aimed to evaluate the potential of image-derived phenotypic traits as proxies for individual fruits weight estimation using multivariate analysis. To this end, an IBP experimentation was carried out on five populations of low-land tomato. Specifically, the Mawar (M; 10 plants), Karina (K; 10 plants), and F2 generation cross (100 lines) samples were used to extract training data for the proposed estimation model, while data derived from M/K//K backcross population (35 lines) and F5 population (50 lines) plants were used for destructive and non-destructive validation, respectively. Several phenotypic traits were extracted from each imaged tomato fruit, including the slice and whole fruit area (FA), round (FR), width (FW), height (FH), and red (RI), green (GI) and blue index (BI), and used as inputs of a genetic- and multivariate-based method for non-destructively predicting its fresh weight (FFW). Based on this research, the whole FA has the greatest potential in predicting tomato FFW regardless to the analyzed cultivar. The relevant model exhibited high power in predicting FFW, as explained by R2-adjusted, R2-deviation and RMSE statistics obtained for calibration (81.30%, 0.20%, 3.14 g, respectively), destructive (69.80%, 0.90%, 4.46 g, respectively) and non-destructive validation (80.20%, 0.50%, 2.12 g, respectively). These results suggest the potential applicability of the proposed IBP approach in guiding field robots or machines for precision harvesting based on non-destructive estimations of fruit weight from image-derived area, thereby enhancing agricultural practices in lowland tomato cultivation. [ABSTRACT FROM AUTHOR]

This article examines the ethical considerations surrounding the use of technology in anatomy education and research. It highlights the need for informed consent when using anatomical images and discusses challenges related to sourcing bodies for teaching and research. The article emphasizes the importance of upholding ethical values in anatomy education, such as obtaining explicit consent and protecting privacy. Additionally, the article "Clin Anat" provides a comprehensive overview of anatomy and its relevance to medical practice, making it a valuable resource for those interested in understanding the human body and its medical implications. [Extracted from the article]

This paper explores the fusion of data science and cognitive techniques in deciphering the behaviors and emotions of farm animals. The focus is on the strategic application of digital imaging and artificial intelligence to discern subtle behavioral patterns and micro-expressions in livestock, offering a predictive window into their emotional states. The significance of acoustic vocalization analysis in interpreting complex communicative signals and emotional subtleties is highlighted. The work extends to cognitive evaluations, such as mirror tests and bias assessments, revealing higher levels of self-awareness and cognitive abilities in farm animals than previously recognized. Emphasizing the need for a synergistic approach, the paper advocates for melding technological advancements with a deep understanding of animal psychology and behavior. This ensures that technology enhances rather than supplants traditional observational methods in animal welfare. The discussion delves into various methodologies and algorithms that measure cognition, underscoring the pivotal role of cognitive computing in advancing animal welfare. A cautious and informed application of these technologies is proposed, emphasizing their role in augmenting, not undermining, the essential human-animal bond. Ultimately, this critical review calls for an ethical, empathetic, and scientifically grounded integration of cognitive computing into animal welfare practices. [ABSTRACT FROM AUTHOR]

Deep learning plays a vital role in precise grapevine disease detection, yet practical applications for farmer assistance are scarce despite promising results. The objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare (Version 1, created by Osama Elsherbiny). This approach utilizes RGB imagery and hybrid deep networks for the detection and prevention of grapevine diseases. Exploring the optimal deep learning architecture involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (including VGG16, VGG19, ResNet50, and ResNet101V2). A gray level co-occurrence matrix (GLCM) was employed to measure the textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. A data augmentation technique was applied to address the issue of limited images. Subsequently, the augmented dataset was used to train the models and enhance their capability to accurately identify and classify plant diseases in real-world scenarios. The analyzed outcomes indicated that the combined CNNRGB-LSTMGLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed the separate deep network and nonaugmented version features. Its validation accuracy, classification precision, recall, and F-measure are all 96.6%, with a 93.4% intersection over union and a loss of 0.123. Furthermore, the software developed through the proposed approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework of the study shows potential for future expansion to include various types of trees. This capability can assist farmers in early detection of tree diseases, enabling them to implement preventive measures. [ABSTRACT FROM AUTHOR]

The continued need to decarbonize buildings has led to a deeper exploration of the use of bamboo culms in building systems. For the design and use of full culm bamboo structures, the bending strength perpendicular-to-fibre has received little comprehensive study along the length of commercially produced bamboo poles. We intensively measured the bending strength perpendicular-to-the-fibre for both node and internode sections along the length of commercially purchased Tre Gai (Bambusa spinosa) culms. Eight culms, each four meters long, were sectioned to measure the bending strength terms including tension and compression strengths in both the N-S (top/bottom) and E/W (left/right) directions using ISO 22157 procedures. Digital scanning was used to measure the inner and outer diameter of each section. Node strength was approximately 2.64 times greater than the internode strength. The performances of perpendicular-to-fibre strength terms were related to the primary tissue strength rather than the bamboo fibre strength within the culm. Based upon observations and measurements, the fmT,NS and fmC,EW strengths had greater values with lower variability, and were less sensitive to changes in length. Implications of perpendicular-to-fibre strength design in building elements are discussed.

Abstract Background Chronic skin wounds are a common complication of many diseases such as diabetes. Various traditional methods for assessing skin wound closure are used in animal studies, including wound tracing, calliper measurements and histological analysis. However, these methods have poorly defined wound closure or practical limitations. Digital image analysis of wounds is an increasingly popular, accessible alternative, but it is unclear whether digital assessment is consistent with traditional methods. This study aimed to optimise and compare digital wound closure assessment with traditional methods, using a diabetic mouse model. Diabetes was induced in male C57BL/6J mice by high-fat diet feeding combined with low dose (65 mg/kg of body weight) streptozotocin injections. Mice fed normal chow were included as controls. After 18 weeks, four circular full-thickness dorsal skin wounds of 4 mm diameter were created per mouse. The wounds were photographed and measured by callipers. Wound closure rate (WCR) was digitally assessed by two reporters using two methods: wound outline (WCR-O) and re-epithelialisation (WCR-E). Wounded skin tissues were collected at 10-days post-wounding and wound width was measured from haematoxylin and eosin-stained skin tissue. Results Between reporters, WCR-O was more consistent than WCR-E, and WCR-O correlated with calliper measurements. Histological analysis supported digital assessments, especially WCR-E, when wounds were histologically closed. Conclusions WCR-O could replace calliper measurements to measure skin wound closure, but WCR-E assessment requires further refinement. Small animal studies of skin wound healing can greatly benefit from standardised definitions of wound closure and more consistent digital assessment protocols.

Although pathologists have their own viewing habits while diagnosing, viewing behaviors leading to the most accurate diagnoses are under-investigated. Digital whole slide imaging has enabled investigators to analyze pathologists visual interpretation of histopathological features using mouse and viewport tracking techniques. In this study, we provide definitions for basic viewing behavior variables and investigate the association of pathologists characteristics and viewing behaviors, and how they relate to diagnostic accuracy when interpreting whole slide images. We use recordings of 32 pathologists actions while interpreting a set of 36 digital whole slide skin biopsy images (5 sets of 36 cases; 180 cases total). These viewport tracking data include the coordinates of a viewport scene on pathologists screens, the magnification level at which that viewport was viewed, as well as a timestamp. We define a set of variables to quantify pathologists viewing behaviors such as zooming, panning, and interacting with a consensus reference panels selected region of interest (ROI). We examine the association of these viewing behaviors with pathologists demographics, clinical characteristics, and diagnostic accuracy using cross-classified multilevel models. Viewing behaviors differ based on clinical experience of the pathologists. Pathologists with a higher caseload of melanocytic skin biopsy cases and pathologists with board certification and/or fellowship training in dermatopathology have lower average zoom and lower variance of zoom levels. Viewing behaviors associated with higher diagnostic accuracy include higher average and variance of zoom levels, a lower magnification percentage (a measure of consecutive zooming behavior), higher total interpretation time, and higher amount of time spent viewing ROIs. Scanning behavior, which refers to panning with a fixed zoom level, has marginally significant positive association with accuracy. Pathologists training, clinical experience, and their exposure to a range of cases are associated with their viewing behaviors, which may contribute to their diagnostic accuracy. Research in computational pathology integrating digital imaging and clinical informatics opens up new avenues for leveraging viewing behaviors in medical education and training, potentially improving patient care and the effectiveness of clinical workflow.

Background/Objectives: Locating the mandibular foramen (MF) through imaging is clinically important for inferior alveolar nerve (IAN) anesthesia and mandibular ramus osteotomies. Although cone-beam computed tomography (CBCT) is superior in imaging the mandible, an orthopantomogram (OPG) is preferred for its ease of use and availability. Therefore, the present study aimed to evaluate the accuracy of digital OPG in localizing the MF, in a subset of the Middle Eastern population. Methods: Radiographic images (OPG and CBCT) of selected patients (adults, dentulous and no mandibular abnormalities) were used to locate the MF through digital measurements (mm) of the anteroposterior distance from the anterior border of the ramus (MF-AP) and the superoinferior position from the mandibular occlusal plane (MF-SI). Measurements were statistically compared between OPG and CBCT for accuracy. Differences in measurements between OPG and CBCT were compared against the anatomic location (right/left), age and biological sex, assuming a p-value < 0.05 as significant. Results: A total of 204 radiographic records (males: 100/females: 104/mean age: 34.65 ± 11.55 years) were evaluated. The measurements for the MF were MF-AP-OPG (right: 13.53 ± 2.44/left: 13.19 ± 2.25), MF-AP-CBCT (right: 13.61 ± 2.39/left: 13.36 ± 2.19), MF-SI-OPG (right: 5.25 ± 1.71/left: 5.41 ± 1.65) and MF-SI-CBCT (right: 5.59 ± 1.66/left: 5.52 ± 1.61). Measurements between OPG and CBCT were not significantly different, except for MF-SI (right) (p = 0.042). While the overall difference between OPG and CBCT (MF-AP/MF-SI) measurements showed a significant association (p < 0.01) with the anatomic location (right/left), a significant association (p < 0.05) with biological sex was observed only for MF-AP. Conclusions: Based on this study’s outcomes, digital OPG is an accurate modality to locate the MF based on anteroposterior (MF-AP) and superoinferior (MF-SI) measurements. This would be clinically beneficial for dental and oral surgeons to achieve the optimum IAN block anesthesia based on preoperative panoramic radiographs. Similarly, it would assist maxillofacial surgeons in planning mandibular orthognathic surgeries and ramus osteotomies without complications.

In order to detect rice false smut quickly and accurately, a lightweight false smut detection model, YOLOv8n-MBS, was proposed in this study. The model introduces the C2f_MSEC module to replace C2f in the backbone network for better extraction of key features of false smut, enhances the feature fusion capability of the neck network for different sizes of false smut by using a weighted bidirectional feature pyramid network, and designs a group-normalized shared convolution lightweight detection head to reduce the number of parameters in the model head to achieve model lightweight. The experimental results show that YOLOv8n-MBS has an average accuracy of 93.9%, a parameter count of 1.4 M, and a model size of 3.3 MB. Compared with the SSD model, the average accuracy of the model in this study increased by 4%, the number of parameters decreased by 89.8%, and the model size decreased by 86.9%; compared with the YOLO series of YOLOv7-tiny, YOLOv5n, YOLOv5s, and YOLOv8n models, the YOLOv8n-MBS model showed outstanding performance in terms of model accuracy and model performance detection; compared to the latest YOLOv9t and YOLOv10n models, the average model accuracy increased by 2.8% and 2.2%, the number of model parameters decreased by 30% and 39.1%, and the model size decreased by 29.8% and 43.1%, respectively. This method enables more accurate and lighter-weight detection of false smut, which provides the basis for intelligent management of rice blast disease in the field and thus promotes food security.

Objective: To determine the reliability of a newly developed software application (Shadent) in natural tooth shade selection. Method and materials: A smartphone device with the self-developed Shadent software (patent no. 201841046815, Intellectual Property of India) recorded tooth shades of the maxillary right central incisor and canine for 250 volunteers. A prospective, clinical, double-blind trial was conducted. The shade match obtained under the standardized condition from Shadent software was compared with the visual method and spectrophotometer. The digital recordings were captured by two master's candidates, while the visual shade match was evaluated by four master's candidates and one faculty member from the Department of Prosthodontics. Crosstab analysis assessed the relationship between the visual method of shade selection, a spectrophotometer, and the Shadent software. Cohen kappa was used to measure the agreement between the methods. Results: A kappa coefficient of 0.59 was observed between the visual assessment and the Shadent software, with an agreement percentage of 64.6%. A kappa coefficient of 0.65 was observed between the spectrophotometer and the Shadent software with an agreement of 69.3%. The software also had a better Cohen kappa agreement with the maxillary central incisor than the maxillary canine. Conclusions: Shadent's reliability was comparable with the visual and spectrophotometric methods and offered repeatability with standardized light intensity. [ABSTRACT FROM AUTHOR]

Abstract Objective Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning. Material and methods As a reference, 1750 OS were manually segmented and labeled. A deep-learning approach based on PointCNN and 3D U-net in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1400 OS. Subsequently, the trained algorithm was applied to a test set consisting of 350 OS. The intersection over union (IoU), as a measure of accuracy, was calculated to quantify the degree of similarity between the annotated ground truth and the model predictions. Results The model achieved accurate teeth segmentations with a mean IoU score of 0.915. The FDI labels of the teeth were predicted with a mean accuracy of 0.894. The optical inspection showed excellent position agreements between the automatically and manually segmented teeth components. Minor flaws were mostly seen at the edges. Conclusion The proposed method forms a promising foundation for time-effective and observer-independent teeth segmentation and labeling on intra-oral scans. Clinical significance Deep learning may assist clinicians in virtual treatment planning in orthodontics, prosthetics, implantology, and orthognathic surgery. The impact of using such models in clinical practice should be explored.