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Objectives: Despite global research on early detection of age-related macular degeneration (AMD), not enough is being done for large-scale screening. Automated analysis of retinal images captured via smartphone presents a potential solution; however, to our knowledge, such an artificial intelligence (AI) system has not been evaluated. The study aimed to assess the performance of an AI algorithm in detecting referable AMD on images captured on a portable fundus camera., Design, Setting: A retrospective image database from the Age-Related Eye Disease Study (AREDS) and target device was used., Participants: The algorithm was trained on two distinct data sets with macula-centric images: initially on 108,251 images (55% referable AMD) from AREDS and then fine-tuned on 1108 images (33% referable AMD) captured on Asian eyes using the target device. The model was designed to indicate the presence of referable AMD (intermediate and advanced AMD). Following the first training step, the test set consisted of 909 images (49% referable AMD). For the fine-tuning step, the test set consisted of 238 (34% referable AMD) images. The reference standard for the AREDS data set was fundus image grading by the central reading centre, and for the target device, it was consensus image grading by specialists., Outcome Measures: Area under receiver operating curve (AUC), sensitivity and specificity of algorithm., Results: Before fine-tuning, the deep learning (DL) algorithm exhibited a test set (from AREDS) sensitivity of 93.48% (95% CI: 90.8% to 95.6%), specificity of 82.33% (95% CI: 78.6% to 85.7%) and AUC of 0.965 (95% CI:0.95 to 0.98). After fine-tuning, the DL algorithm displayed a test set (from the target device) sensitivity of 91.25% (95% CI: 82.8% to 96.4%), specificity of 84.18% (95% CI: 77.5% to 89.5%) and AUC 0.947 (95% CI: 0.911 to 0.982)., Conclusion: The DL algorithm shows promising results in detecting referable AMD from a portable smartphone-based imaging system. This approach can potentially bring effective and affordable AMD screening to underserved areas., Competing Interests: Competing interests: FMS, DPR, TJK, BO and ASi are employees of Remidio Innovative Solutions. Remidio Innovative Solutions, Inc, USA and Medios Technologies are wholly owned subsidiaries of Remidio Innovative Solutions Pvt Ltd, India. FMS has patents and financial interests in Remidio Innovative Solutions Pvt Ltd (ESOP and Stock). ASi has patents, stock and leadership role. ASh is a consultant for Novartis, Allergan, Bayer and Intas, unrelated to this work., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Researchers have developed machine learning-based ECG diagnostic algorithms that match or even surpass cardiologist level of performance. However, most of them cannot be used in real-world, as older generation ECG machines do not permit installation of new algorithms., Objective: To develop a smartphone application that automatically extract ECG waveforms from photos and to convert them to voltage-time series for downstream analysis by a variety of diagnostic algorithms built by researchers., Methods: A novel approach of using objective detection and image segmentation models to automatically extract ECG waveforms from photos taken by clinicians was devised. Modular machine learning models were developed to sequentially perform waveform identification, gridline removal, and scale calibration. The extracted data were then analysed using a machine learning-based cardiac rhythm classifier., Results: Waveforms from 40 516 scanned and 444 photographed ECGs were automatically extracted. 12 828 of 13 258 (96.8%) scanned and 5399 of 5743 (94.0%) photographed waveforms were correctly cropped and labelled. 11 604 of 12 735 (91.1%) scanned and 5062 of 5752 (88.0%) photographed waveforms achieved successful voltage-time signal extraction after automatic gridline and background noise removal. In a proof-of-concept demonstration, an atrial fibrillation diagnostic algorithm achieved 91.3% sensitivity, 94.2% specificity, 95.6% positive predictive value, 88.6% negative predictive value and 93.4% F1 score, using photos of ECGs as input., Conclusion: Object detection and image segmentation models allow automatic extraction of ECG signals from photos for downstream diagnostics. This novel pipeline circumvents the need for costly ECG hardware upgrades, thereby paving the way for large-scale implementation of machine learning-based diagnostic algorithms., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose To compare the quality of optic nerve photographs from three different handheld fundus cameras and to assess the reproducibility and agreement of vertical cup-to-disk ratio (VCDR) measurements from each camera. Methods Adult patients from a comprehensive ophthalmology clinic and an intravitreous injection clinic in northern Thailand were recruited for this cross-sectional study. Each participant had optic nerve photography performed with each of 3 handheld cameras: the Volk iNview, Volk Pictor Plus, and Peek Retina. Images were graded for VCDR in a masked fashion by two photo-graders and images with > 0.2 discrepancy in VCDR were assessed by a third photo-grader. Results A total of 355 eyes underwent imaging with three different handheld fundus cameras. Optic nerve images were judged ungradable in 130 (37%) eyes imaged with Peek Retina, compared to 36 (10%) and 55 (15%) eyes imaged with the iNview and Pictor Plus, respectively. For 193 eyes with gradable images from all 3 cameras, inter-rater reliability for VCDR measurements was poor or moderate for each of the cameras, with intraclass correlation coefficients ranging from 0.41 to 0.52. A VCDR ≥ 0.6 was found in 6 eyes on iNview images, 9 eyes on Pictor Plus images, and 3 eyes on Peek images, with poor agreement between cameras (e.g., no eyes graded as VCDR ≥ 0.6 on images from both the iNview and Pictor Plus). Conclusions Inter-rater reliability of VCDR grades from 3 handheld cameras was poor. Cameras did not agree on which eyes had large VCDRs.

Objective: Low-cost, portable RGB-D cameras with integrated motion tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications, particularly when applied to children. In previous work, we developed an RGB-D camera-based motion tracking method and showed that it accurately captures body joint positions of children and young adults in 3D. In this study, the validity and accuracy of clinically relevant motion parameters that were computed from kinematics of our motion tracking method are evaluated in children and young adults., Methods: Twenty-three typically developing children and healthy young adults (5-29 years, 110-189 cm) performed five movement tasks while being recorded simultaneously with a marker-based Vicon system and an Azure Kinect RGB-D camera. Motion parameters were computed from the extracted kinematics of both methods: time series measurements, i.e., measurements over time, peak measurements, i.e., measurements at a single time instant, and movement smoothness. The agreement of these parameter values was evaluated using Pearson's correlation coefficients r for time series data, and mean absolute error (MAE) and Bland-Altman plots with limits of agreement for peak measurements and smoothness., Results: Time series measurements showed strong to excellent correlations (r-values between 0.8 and 1.0), MAE for angles ranged from 1.5 to 5 degrees and for smoothness parameters (SPARC) from 0.02-0.09, while MAE for distance-related parameters ranged from 9 to 15 mm., Conclusion: Extracted motion parameters are valid and accurate for various movement tasks in children and young adults, demonstrating the suitability of our tracking method for clinical motion analysis., Clinical Impact: The low-cost portable hardware in combination with our tracking method enables motion analysis outside of specialized facilities while providing measurements that are close to those of the clinical gold-standard., (© 2024 The Authors.)

Smartphones are increasingly relevant resources in medical practice as they are ubiquitous and reasonably cheap. Among the advantages of using smartphones in medical practise, there is the possibility of obtaining reproducible photographic documentation of various conditions. This is particularly true in the ophthalmic field, where anterior segment color photography plays a significant role in the diagnosis and the management of ocular surface diseases. Here we propose an original design for an open-source smartphone accessory for taking and sharing high-definition photographs of the anterior segment. It can be easily reproduced via 3D printing, and it only needs to be integrated with an intraocular lens (IOL), widely available to the majority of ophthalmologists. Compared to other solutions described previously, it allows a precise and reproducible placement of the IOL on the smartphone camera, avoiding manual positioning that could result tricky and time-consuming. The IOL holder is cheap, scalable, portable and it can be quickly assembled and disassembled, without permanently modifying the smartphone camera., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Despite the growing interest in mirrorless vehicles equipped with a camera monitor system (CMS), the human factors research findings on CMS display layout design have not been synthesized yet, hindering the application of the knowledge and the identification of future research directions. In an effort to address the 'lack of integration of the existing knowledge', this literature review addresses the following research questions: 1) what CMS display layout designs have been considered/developed by academic researchers and by automakers, respectively?; 2) among possible CMS display layout design alternatives, which ones have not yet been examined through human factors evaluation studies?; and 3) how do the existing human factors studies on the evaluation of different CMS display layout designs vary in the specifics of research? This review provides significant implications for the ergonomic design of CMS display layouts, including some potential design opportunities and future research directions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved treatment of both diabetes and DR have amended visual prognosis for DR. The number of patients with diabetes is increasing and telemedicine, mobile handheld devices and automated solutions may alleviate the burden for healthcare. We compared the performance of 21 artificial intelligence (AI) algorithms for referable DR screening in datasets taken by handheld Optomed Aurora fundus camera in a real-world setting., Patients and Methods: Prospective study of 156 patients (312 eyes) attending DR screening and follow-up. Both papilla- and macula-centred 50° fundus images were taken from each eye. DR was graded by experienced ophthalmologists and 21 AI algorithms., Results: Most eyes, 183 out of 312 (58.7%), had no DR and mild NPDR was noted in 21 (6.7%) of the eyes. Moderate NPDR was detected in 66 (21.2%) of the eyes, severe NPDR in 1 (0.3%), and PDR in 41 (13.1%) composing a group of 34.6% of eyes with referable DR. The AI algorithms achieved a mean agreement of 79.4% for referable DR, but the results varied from 49.4% to 92.3%. The mean sensitivity for referable DR was 77.5% (95% CI 69.1-85.8) and specificity 80.6% (95% CI 72.1-89.2). The rate for images ungradable by AI varied from 0% to 28.2% (mean 1.9%). Nineteen out of 21 (90.5%) AI algorithms resulted in grading for DR at least in 98% of the images., Conclusions: Fundus images captured with Optomed Aurora were suitable for DR screening. The performance of the AI algorithms varied considerably emphasizing the need for external validation of screening algorithms in real-world settings before their clinical application.

The Pedestrian Collision Avoidance System (PCAS) of Intelligent Vehicle (IV) can be effective in preventing the occurrence of traffic accidents. However, the complicated operation environments introduce great challenges to the camera used by the PCAS. Therefore, the camera based PCAS should be fully tested and evaluated before deployment. The traditional simulation test for the camera based PCAS attempted to use geometric or physical simulation models, which have low reality and are suitable for the primary stage of the PCAS development. Camera-in-the-Loop (CIL) test is one of Hardware-in-the-Loop methods that embeds the real camera hardware into the virtual simulation system to test the camera. CIL can utilize the real hardware response while overcoming the common simulation weakness of fidelity. In this paper, we construct a CIL test platform, and propose the CIL based test scenarios generation and scenario parameter impact evaluation method for PCAS. First, we construct the CIL test platform whose image quality and functional confidence are both validated to prove CIL credibility. Second, the PCAS under the test is analyzed and the corresponding test scenario parameters are designed. In order to accelerate the test scenario generation, a Greedy Based Combination test method (GBC) based on the CIL is proposed. The Chi-square analysis and two-factor of variance analysis verification methods are used to analyze the influence of individual and multiple scenario parameters on the PCAS performance. The experiment results show that the GBC improves the test speed by 12 times compared to the traversal test, and the frequency ratio of each scenario parameter is no more than 3% different from that of the traversal test. Also, GBC has an equivalent ability to find the PCAS collision scenarios parameter combination to the traversal test., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Checking each item placed in a separate collection bin of recyclables to examine contamination is often difficult for a researcher relying on such data. This is because of the time and inconvenience involved to manually identify items. We test a proof-of-concept experiment on the ability of trail cameras to identify items placed within separate collection bins. After a pre-test of seven camera models, we selected one with the best image quality. We use this camera for lab and field trials to count the number of identifiable items based on photos compared to manual hand-counts of the items. Three lab trials of this camera resulted in an average of 82% accuracy in item identification. We then conducted a field experiment, testing photo quality to identify items in six separate collection bins across a university campus over a one-month period with a total of over 9,700 photos. Of the 1343 items placed in the separate collection bins, the trail cameras provided photographs of high enough quality such that successful identification occurred for 68.5% of the items, with poor identification for paper items and small items. We conclude that trail cameras can be useful for data collection in separate collection behavior, especially for items with the largest surface size greater than a credit card., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Enhancing detection of cryptic snakes is critical for the development of conservation and management strategies; yet, finding methods that provide adequate detection remains challenging. Issues with detecting snakes can be particularly problematic for some species, like the invasive Burmese python ( Python bivittatus ) in the Florida Everglades., Methods: Using multiple survey methods, we predicted that our ability to detect pythons, larger snakes and all other snakes would be enhanced with the use of live mammalian lures (domesticated rabbits; Oryctolagus cuniculus ). Specifically, we used visual surveys, python detection dogs, and time-lapse game cameras to determine if domesticated rabbits were an effective lure., Results: Time-lapse game cameras detected almost 40 times more snakes ( n = 375, treatment = 245, control = 130) than visual surveys ( n = 10). We recorded 21 independent detections of pythons at treatment pens (with lures) and one detection at a control pen (without lures). In addition, we found larger snakes, and all other snakes were 165% and 74% more likely to be detected at treatment pens compared to control pens, respectively. Time-lapse cameras detected almost 40 times more snakes than visual surveys; we did not detect any pythons with python detection dogs., Conclusions: Our study presents compelling evidence that the detection of snakes is improved by coupling live mammalian lures with time-lapse game cameras. Although the identification of smaller snake species was limited, this was due to pixel resolution, which could be improved by changing the camera focal length. For larger snakes with individually distinctive patterns, this method could potentially be used to identify unique individuals and thus allow researchers to estimate population dynamics., Competing Interests: McKayla M. Spencer is employed by Florida Fish and Wildlife Conservation Commission, and Kristen Hart is employed by United States Geological Survey.

Introduction: One of the challenges faced by the Colombian Health System is to improve access to health services for the dispersed and isolated rural population, particularly in the field of dermatology. This article examines the implementation of a teledermatology service using a PC and camera versus smartphone technology. Methods: A total of 542 teledermatology visits were conducted, involving 478 patients, in addition to 64 visits for clinical follow-up for patients as per the dermatologist's recommendation. Out of the 478 patients, 461 met the inclusion criteria and agreed to participate in the study. The data collection instrument from the general practitioner or referring provider covered three consultation moments: (1) sending an initial consultation, (2) providing a response to the patient, and (3) sending a follow-up consultation. Seven hundred forty-seven records were completed by the general practitioner for the three consultation moments. Furthermore, 372 consultations were documented by the dermatologist or referring provider for two moments: (1) response to the initial consultation by the dermatologist, and (2) response to the follow-up consultation by the dermatologist. After validating the information reported in the instruments, a descriptive analysis of the data was conducted, utilizing absolute frequencies and percentages for qualitative variables and measures of central tendency (mean, median, standard deviation, and interquartile range) for quantitative variables. The data were analyzed from 747 records of the referring provider instrument related to 461 patients, between 18 and 98 years of age, with a predominantly female representation. Results: The results indicated that for teleconsultations conducted using a mobile device, the average total duration of the teleconsultation was longer on the traditional platform compared with the mobile device (13.03 vs. 8.27 min). Additionally, it was observed that the time taken to store, send, and capture a single image (clinical or dermoscopic) using the mobile device was three times lower than that on the conventional platform (25 vs. 75 s). Similar findings were noted for teleconsultations carried out by the dermatologist, predominantly utilizing a mobile device. The average consultation time was shorter for the mobile device compared with the traditional platform (8.14 vs. 12 min). Conclusions: The cost reduction suggests that the operation of the service is more efficient with smartphone technology in comparison to the use of a PC and camera. Teledermatology with smartphones provides a streamlined, efficient, and technically sound process for obtaining clinical and dermoscopic images.

Multifunctional landscapes that support economic activities and conservation of biological diversity (e.g., cattle ranches with native forest) are becoming increasingly important because small remnants of native forest may comprise the only habitat left for some wildlife species. Understanding the co-occurrence between wildlife and disturbance factors, such as poaching activity and domesticated ungulates, is key to successful management of multifunctional landscapes. Tools to measure co-occurrence between wildlife and disturbance factors include camera traps and autonomous acoustic recording units. We paired 52 camera-trap stations with acoustic recorders to investigate the association between 2 measures of disturbance (poaching and cattle) and wild ungulates present in multifunctional landscapes of the Colombian Orinoquía. We used joint species distribution models to investigate species-habitat associations and species-disturbance correlations. One model was fitted using camera-trap data to detect wild ungulates and disturbance factors, and a second model was fitted after replacing camera-trap detections of disturbance factors with their corresponding acoustic detections. The direction, significance, and precision of the effect of covariates depended on the sampling method used for disturbance factors. Acoustic monitoring typically resulted in more precise estimates of the effects of covariates and of species-disturbance correlations. Association patterns between wildlife and disturbance factors were found only when disturbance was detected by acoustic recorders. Camera traps allowed us to detect nonvocalizing species, whereas audio recording devices increased detection of disturbance factors leading to more precise estimates of co-occurrence patterns. The collared peccary (Pecari tajacu), lowland tapir (Tapirus terrestris), and white-tailed deer (Odocoileus virginianus) co-occurred with disturbance factors and are conservation priorities due to the greater risk of poaching or disease transmission from cattle., (© 2023 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

Background: We previously developed a machine learning (ML)-assisted system for predicting the clinical activity score (CAS) in thyroid-associated orbitopathy (TAO) using digital facial images taken by a digital single-lens reflex camera in a studio setting. In this study, we aimed to apply this system to smartphones and detect active TAO (CAS ≥3) using facial images captured by smartphone cameras. We evaluated the performance of our system on various smartphone models and compared it with the performance of ophthalmologists with varying clinical experience. Methods: We applied the preexisting ML architecture to classify photos taken with smartphones (Galaxy S21 Ultra, iPhone 12 pro, iPhone 11, iPhone SE 2020, Galaxy M20, and Galaxy A21S). The performance was evaluated with smartphone-captured images from 100 patients with TAO. Three ophthalmology residents, three general ophthalmologists with <5 years of clinical experience, and three oculoplastic specialists independently interpreted the same set of images taken under a studio environment and compared their results with those generated by the smartphone-based ML-assisted system. Reference CAS was determined by a consensus of three oculoplastic specialists. Results: Active TAO (CAS ≥3) was identified in 28 patients. Smartphone model used in capturing facial images influenced active TAO detection performance (F1 score 0.59-0.72). The smartphone-based system showed 74.5% sensitivity, 84.8% specificity, and F1 score 0.70 on top three smartphones. On images from all six smartphones, average sensitivity, specificity, and F1 score were 71.4%, 81.6%, and 0.66, respectively. Ophthalmology residents' values were 69.1%, 55.1%, and 0.46. General ophthalmologists' values were 61.9%, 79.6%, and 0.55. Oculoplastic specialists' values were 73.8%, 90.7%, and 0.75. This smartphone-based ML-assisted system predicted CAS within 1 point of reference CAS in 90.7% using facial images from smartphones. Conclusions: Our smartphone-based ML-assisted system shows reasonable accuracy in detecting active TAO, comparable with oculoplastic specialists and outperforming residents and general ophthalmologists. It may enable reliable self-monitoring for disease activity, but confirmatory research is needed for clinical application.

Aim: To assess the performance of smartphone based wide-field retinal imaging (WFI) versus ultra-wide-field imaging (UWFI) for assessment of sight-threatening diabetic retinopathy (STDR) as well as locating predominantly peripheral lesions (PPL) of DR., Methods: Individuals with type 2 diabetes with varying grades of DR underwent nonmydriatic UWFI with Daytona Plus camera followed by mydriatic WFI with smartphone-based Vistaro camera at a tertiary care diabetes centre in South India in 2021-22. Grading of DR as well as identification of PPL (DR lesions beyond the posterior pole) in the retinal images of both cameras was performed by senior retina specialists. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The sensitivity and specificity of smartphone based WFI for detection of PPL and STDR was assessed. Agreement between the graders for both cameras was compared., Results: Retinal imaging was carried out in 318 eyes of 160 individuals (mean age 54.7 ± 9 years; mean duration of diabetes 16.6 ± 7.9 years). The sensitivity and specificity for detection of STDR by Vistaro camera was 92.7% (95% CI 80.1-98.5) and 96.6% (95% CI 91.5-99.1) respectively and 95.1% (95% CI 83.5-99.4) and 95.7% (95% CI 90.3-98.6) by Daytona Plus respectively. PPL were detected in 89 (27.9%) eyes by WFI by Vistaro camera and in 160 (50.3%) eyes by UWFI. However, this did not translate to any significant difference in the grading of STDR between the two imaging systems. In both devices, PPL were most common in supero-temporal quadrant (34%). The prevalence of PPL increased with increasing severity of DR with both cameras (p < 0.001). The kappa comparison between the 2 graders for varying grades of severity of DR was 0.802 (p < 0.001) for Vistaro and 0.753 (p < 0.001) for Daytona Plus camera., Conclusion: Mydriatic smartphone-based widefield imaging has high sensitivity and specificity for detecting STDR and can be used to screen for peripheral retinal lesions beyond the posterior pole in individuals with diabetes., (© 2024. The Author(s).)

Background/aims: To determine agreement of one-field (1F, macula-centred), two-field (2F, disc-macula) and five-field (5F, macula, disc, superior, inferior and nasal) mydriatic handheld retinal imaging protocols for the assessment of diabetic retinopathy (DR) as compared with standard seven-field Early Treatment Diabetic Retinopathy Study (ETDRS) photography., Methods: Prospective, comparative instrument validation study. Mydriatic retinal images were taken using three handheld retinal cameras: Aurora (AU; 50° field of view (FOV), 5F), Smartscope (SS; 40° FOV, 5F), and RetinaVue (RV; 60° FOV, 2F) followed by ETDRS photography. Images were evaluated at a centralised reading centre using the international DR classification. Each field protocol (1F, 2F and 5F) was graded independently by masked graders. Weighted kappa (Kw) statistics assessed agreement for DR. Sensitivity (SN) and specificity (SP) for referable diabetic retinopathy (refDR; moderate non-proliferative diabetic retinopathy (NPDR) or worse, or ungradable images) were calculated., Results: Images from 225 eyes of 116 patients with diabetes were evaluated. Severity by ETDRS photography: no DR, 33.3%; mild NPDR, 20.4%; moderate, 14.2%; severe, 11.6%; proliferative, 20.4%. Ungradable rate for DR: ETDRS, 0%; AU: 1F 2.23%, 2F 1.79%, 5F 0%; SS: 1F 7.6%, 2F 4.0%, 5F 3.6%; RV: 1F 6.7%, 2F 5.8%. Agreement rates of DR grading between handheld retinal imaging and ETDRS photography were (Kw, SN/SP refDR) AU: 1F 0.54, 0.72/0.92; 2F 0.59, 0.74/0.92; 5F 0.75, 0.86/0.97; SS: 1F 0.51, 0.72/0.92; 2F 0.60, 0.75/0.92; 5F 0.73, 0.88/0.92; RV: 1F 0.77, 0.91/0.95; 2F 0.75, 0.87/0.95., Conclusion: When using handheld devices, the addition of peripheral fields decreased the ungradable rate and increased SN and SP for refDR. These data suggest the benefit of additional peripheral fields in DR screening programmes that use handheld retinal imaging., Competing Interests: Competing interests: RPS, CMPJ, LACA, CMGS, AVS, GPA, AR and MA: no financial disclosures; TP: financial support from Optomed; personal fees from Novartis, Bayer, Roche, Heidelberg and Optos, outside of the submitted work; PSS: financial support from Optomed and Hillrom., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background: Ophthalmoscopy is a valuable tool in clinical practice. We report the use of a novel smartphone-based handheld device for visualisation and photo-documentation of the ocular fundus in veterinary medicine., Methods: Selected veterinary patients of a referral ophthalmology service were included if one or both eyes had clear ocular media, allowing for examination of the fundus. Following pharmacological mydriasis, fundic images were obtained with a handheld fundus camera (Volk VistaView). For comparison, the fundus of a subset of animals was also imaged with a veterinary-specific fundus camera (Optomed Smartscope VET2)., Results: The large field of view achieved by the Volk VistaView allowed for rapid and thorough observation of the ocular fundus in animals, providing a tool to visualise and record common pathologies of the posterior segment. Captured fundic images were sometimes overexposed, with the tapetal fundus artificially appearing hyperreflective when using the Volk VistaView camera, a finding that was less frequent when activating a 'veterinary mode' that reduced the sensitivity of the camera's sensor. The Volk VistaView compared well with the Optomed Smartscope VET2., Limitation: The main study limitation was the small sample size., Conclusions: The Volk VistaView camera was easy to use and provided good-quality fundic images in veterinary patients with healthy or diseased eyes, offering a wide field of view that was ideal for screening purposes., (© 2024 The Authors. Veterinary Record published by John Wiley & Sons Ltd on behalf of British Veterinary Association.)

This paper presents an experimental evaluation of a wearable light-emitting diode (LED) transmitter in an optical camera communications (OCC) system. The evaluation is conducted under conditions of controlled user movement during indoor physical exercise, encompassing both mild and intense exercise scenarios. We introduce an image processing algorithm designed to identify a template signal transmitted by the LED and detected within the image. To enhance this process, we utilize the dynamics of controlled exercise-induced motion to limit the tracking process to a smaller region within the image. We demonstrate the feasibility of detecting the transmitting source within the frames, and thus limit the tracking process to a smaller region within the image, achieving an reduction of 87.3% for mild exercise and 79.0% for intense exercise.

Invasive plant species pose ecological threats to native ecosystems, particularly in areas adjacent to roadways, considering that roadways represent lengthy corridors through which invasive species can propagate. Traditional manual survey methods for monitoring invasive plants are labor-intensive and limited in coverage. This paper introduces a high-speed camera system, named CamAlien, designed to be mounted on vehicles for efficient invasive plant species monitoring along roadways. The camera system captures high-quality images at rapid intervals, to monitor the full roadside when following traffic speed. The system utilizes a global shutter sensor to reduce distortion and geotagging for precise localistion. The camera system makes it possible to collect extensive data sets, which can be used for a digital library of the invasive species and their locations, but also subsequent training of machine learning algorithms for automated species recognition., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Dyrmann M et al.)

Background Fundus examination is an integral part of the clinical evaluation of patients with medical emergencies. It is done at the bedside using a portable direct ophthalmoscope. Smartphone fundus photography (SFP) is a novel technique of retinal imaging. We evaluated the use of SFP by a physician in medical emergencies and compared it with direct ophthalmoscopy (DO) findings of the ophthalmologist. Methods We did a prospective study on patients admitted with medical emergencies with an indication for fundus examination. The SFP was done by the physician, and its findings were noted. These were compared with the DO findings of the ophthalmologist. Results Of the 182 patients studied, 111 (61%) had fundus findings by SFP and 95 (52.5%) by DO. Papilloedema (21.4%), haemorrhages (20%) and Roth spots (12.5%) were most common. DO missed early papilloedema and findings in the peripheral retina. Conclusions SFP is as effective as DO, in detecting retinal findings in patients with medical emergencies and can be performed at the bedside by the physician.

Square markers are a widespread tool to find correspondences for camera localization because of their robustness, accuracy, and detection speed. Their identification is usually based on a binary encoding that accounts for the different rotations of the marker; however, most systems do not consider the possibility of observing reflected markers. This case is possible in environments containing mirrors or reflective surfaces, and its lack of consideration is a source of detection errors, which is contrary to the robustness expected from square markers. This is the first work in the literature that focuses on reflection-aware square marker dictionaries. We present the derivation of the inter-marker distance of a reflection-aware dictionary and propose new algorithms for generating and identifying such dictionaries. Additionally, part of the proposed method can be used to optimize preexisting dictionaries to take reflection into account. The experimentation carried out demonstrates how our proposal greatly outperforms the most popular predefined dictionaries in terms of inter-marker distance and how the optimization process significantly improves them.

Simultaneous monitoring of overlapped multi-wing structure by stereo-digital image correlation (stereo-DIC) may be used to quantify insect motion and deformation. We propose a dual stereo-DIC system based on multispectral imaging with a polarization RGB camera. Different fluorescent speckle patterns were fabricated on wings, which emit red and blue spectra under ultraviolet light that were imaged and separated using a polarization RGB camera and auxiliary optical splitting components. The resulting dual stereo-DIC system was validated through translation experiments with transparent sheets and reconstructed overlapped insect wings (cicadas). Dynamic measurements of the Ruban artificial flier indicate the efficacy of this approach to determining real insect flight behavior.

Purpose: Proctored surgical instruction has traditionally been taught through in-person interactions in either the operating room or an improvised wet lab. Because of the COVID-19 pandemic, live in-person instruction was not feasible owing to social distancing protocols, so a virtual wet lab (VWL) was proposed and implemented. The purpose of this article is to describe our experience with a VWL as a Descemet membrane endothelial keratoplasty (DMEK) skills-transfer course. This is the first time that a VWL environment has been described for the instruction of ophthalmic surgery., Methods: Thirteen participant surgeons took part in VWLs designed for DMEK skills transfer in September and October 2020. A smartphone camera adapter and a video conference software platform were the unique media for the VWL. After a didactic session, participants were divided into breakout rooms where their surgical scope view was broadcast live, allowing instructors to virtually proctor their participants in real time. Participants were surveyed to assess their satisfaction with the course., Results: All (100%) participants successfully injected and unfolded their DMEK grafts. Ten of the 13 participants completed the survey. Respondents rated the experience highly favorably., Conclusions: With the use of readily available technology, VWLs can be successfully implemented in lieu of in-person skills-transfer courses. Further development catering to the needs of the participant might allow VWLs to serve as a viable option of surgical education, currently limited by geographical and social distancing boundaries., Competing Interests: C. A. Lopes is employed by Eversight and serves as Partner Relations Director. E. A. Hellier is employed by Eversight and serves as Global Development Director. D. S. Chu serves as a medical director for Eversight, but receives no financial compensation. He is a paid consultant to Aldeyra, Dompé, and Mallinckrodt. He is also on the Speaker Bureau of Abbvie, Novartis and Mallinckrodt, and he has received a research grant from Mallinckrodt. L. J. Cervantes serves as a medical director and sits on the board of directors of Eversight, but receives no financial compensation. The remaining author has no conflicts of interest to disclose., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Retinopathy of Prematurity (ROP) is a prominent source of low vision and blindness in preterm babies. Wide-Field Digital Retinal Imaging (WFDRI) systems acquire accurate digital images which are very useful for identification, documentation and transmitting the various retinal diseases. This telemedicine technique has potential for an alternative tool for Binocular Indirect Ophthalmoscopy (BIO) in ROP screening, but it is very expensive and accessibility for poor communities is limited. Capabilities of good illumination, high resolution camera and processing speed of the modern smartphones are being identified as a substitute. Potential applications of 3D printing is that it provides a severe impact in medical field, especially in ophthalmology sector. Competences of 3D printing are very useful for the development of retinal camera from any smartphone with the help of 3D printable devices. The primary aim of this study is to develop a handheld 3D printed smartphone-based multi-purpose fundus camera for ROP screening. The secondary aim is to check the feasibility and compare the digital fundus images obtained from the developed fundus camera against the commercial RetCam imaging. The proposed cost effective and remote reading device is an alternative to WFDRI for ROP screening and can improve the potential of ROP care for low resource communities., (© 2021. Biomedical Engineering Society.)

Background: As the prevalence of trachoma declines worldwide, it is becoming increasingly expensive and challenging to standardize graders in the field for surveys to document elimination. Photography of the tarsal conjunctiva and remote interpretation may help alleviate these challenges. The purpose of this study was to develop, and field test an Image Capture and Processing System (ICAPS) to acquire hands-free images of the tarsal conjunctiva for upload to a virtual reading center for remote grading., Methodology/principal Findings: This observational study was conducted during a district-level prevalence survey for trachomatous inflammation-follicular (TF) in Chamwino, Tanzania. The ICAPS was developed using a Samsung Galaxy S8 smartphone, a Samsung Gear VR headset, a foot pedal trigger and customized software allowing for hands-free photography. After a one-day training course, three trachoma graders used the ICAPS to collect images from 1305 children ages 1-9 years, which were expert-graded remotely for comparison with field grades. In our experience, the ICAPS was successful at scanning and assigning barcodes to images, focusing on the everted eyelid with adequate examiner hand visualization, and capturing images with sufficient detail to grade TF. The percentage of children with TF by photos and by field grade was 5%. Agreement between grading of the images compared to the field grades at the child level was kappa = 0.53 (95%CI = 0.40-0.66). There were ungradable images for at least one eye in 199 children (9.1%), with more occurring in children ages 1-3 (18.5%) than older children ages 4-9 (4.2%) (χ2 = 145.3, p<0.001)., Conclusions/significance: The prototype ICAPS device was robust, able to image 1305 children in a district level survey and transmit images from rural Tanzania to an online grading platform. More work is needed to improve the percentage of ungradable images and to better understand the causes of disagreement between field and photo grading., Competing Interests: The authors have declared that no competing interests exist.

Effective and accurate in vivo diagnosis of retinal pathologies requires high performance imaging devices, combining a large field of view and the ability to discriminate the ballistic signal from the diffuse background in order to provide a highly contrasted image of the retinal structures. Here, we have implemented the partial-field illumination ophthalmoscope, a patterned illumination modality, integrated to a high pixel rate adaptive optics full-field microscope. This non-invasive technique enables us to mitigate the low signal-to-noise ratio, intrinsic of full-field ophthalmoscopes, by partially illuminating the retina with complementary patterns to reconstruct a wide-field image. This new, to the best of our knowledge, modality provides an image contrast spanning from the full-field to the confocal contrast, depending on the pattern size. As a result, it offers various trade-offs in terms of contrast and acquisition speed, guiding the users towards the most efficient system for a particular clinical application.

Purpose: In today's life, many electronic gadgets have the potential to become invaluable health care devices in future. The gadgets in this category include smartphones, smartwatches, and others. Till now, smartphone role has been highlighted on many occasions in different areas, and they continue to possess immense role in clinical documentation, clinical consultation, and digitalization of ocular care. In last one decade, many treatable conditions including diabetic retinopathy, glaucoma, and other pediatric retinal diseases are being imaged using smartphones. Methods: To comprehend this cumulative knowledge, a detailed medical literature search was conducted on PubMed/Medline, Scopus, and Web of Science till February 2021. Results: The included literature revealed a definitive progress in posterior segment imaging. From simple torch light with smartphone examination to present day compact handy devices with artificial intelligence integrated software's have changed the very perspectives of ocular imaging in ophthalmology. The consistently reproducible results, constantly improving imaging techniques, and most importantly their affordable costs have renegotiated their role as effective screening devices in ophthalmology. Moreover, the obtained field of view, ocular safety, and their key utility in non-ophthalmic specialties are also growing. Conclusions: To conclude, smartphone imaging can now be considered as a quick, cost-effective, and digitalized tool for posterior segment screenings, however, their definite role in routine ophthalmic clinics is yet to be established.

Purpose : To evaluate rotational stability, tilt and decentration of a new monofocal IOL with a 7.0 mm optic and frame haptics. Methods: Prospective post-market clinical follow-up study at the Kepler University Hospital Linz, Austria. An Aspira-aXA (HumanOptics, Germany) was implanted in 74 eyes of 42 cataract patients. The lens was manufactured with toric markings. IOL rotational stability was evaluated by comparing its position at the end of surgery (EoS) versus 1 day, 1 week, 1 month and 4 months postoperatively. IOL tilt and decentration were measured using a Scheimpflug camera at 1 week, 1 month and 4 months. Results: Median absolute IOL rotation was 1.42 degrees (n = 52; mean = 2.18 ± 2.23°) within 1 day after surgery and was significantly higher compared to all later intervals (median <1.0 degree; P = .001). At the 4 months follow-up, IOL rotation was within 5.0 degrees in 85% of the eyes (n = 40) and within 10.0 degrees in 98% (n = 46) of the eyes. The only eye with an IOL rotation of ≥ 10.ty0 degrees (EoS vs . 1 day) had an AL of 26.45 mm. At the last follow-up, the IOL vertical and horizontal tilt referenced to the pupillary axis was in average less than 1.5 degrees in both eyes (n = 54; maximum 5.85°). Decentration in both meridians was on average less than 0.10 mm in both eyes (maximum 0.30 mm). Conclusion: The one-piece Aspira-aXA IOL showed good and stable positioning within the capsular bag over a 4 months period.

Purpose: Stage is an important feature to identify in retinal images of infants at risk of retinopathy of prematurity (ROP). The purpose of this study was to implement a convolutional neural network (CNN) for binary detection of stages 1, 2, and 3 in ROP and to evaluate its generalizability across different populations and camera systems., Design: Diagnostic validation study of CNN for stage detection., Participants: Retinal fundus images obtained from preterm infants during routine ROP screenings., Methods: Two datasets were used: 5943 fundus images obtained by RetCam camera (Natus Medical, Pleasanton, CA) from 9 North American institutions and 5049 images obtained by 3nethra camera (Forus Health Incorporated, Bengaluru, India) from 4 hospitals in Nepal. Images were labeled based on the presence of stage by 1 to 3 expert graders. Three CNN models were trained using 5-fold cross-validation on datasets from North America alone, Nepal alone, and a combined dataset and were evaluated on 2 held-out test sets consisting of 708 and 247 images from the Nepali and North American datasets, respectively., Main Outcome Measures: Convolutional neural network performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, and specificity., Results: Both the North American- and Nepali-trained models demonstrated high performance on a test set from the same population: AUROC, 0.99; AUPRC, 0.98; sensitivity, 94%; and AUROC, 0.97; AUPRC, 0.91; and sensitivity, 73%; respectively. However, the performance of each model decreased to AUROC of 0.96 and AUPRC of 0.88 (sensitivity, 52%) and AUROC of 0.62 and AUPRC of 0.36 (sensitivity, 44%) when evaluated on a test set from the other population. Compared with the models trained on individual datasets, the model trained on a combined dataset achieved improved performance on each respective test set: sensitivity improved from 94% to 98% on the North American test set and from 73% to 82% on the Nepali test set., Conclusions: A CNN can identify accurately the presence of ROP stage in retinal images, but performance depends on the similarity between training and testing populations. We demonstrated that internal and external performance can be improved by increasing the heterogeneity of the training dataset features of the training dataset, in this case by combining images from different populations and cameras., (Copyright © 2020 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency of earthworms in ecology varies along with species. Therefore, the role of taxonomy in earthworm study is significant. The taxonomy of earthworms cannot reliably be established through morphological characteristics because the small and simple body plan of the earthworm does not have anatomical complex and highly specialized structures. Recently, molecular techniques have been adopted to accurately classify the earthworm species but these techniques are time-consuming and costly. To combat this issue, in this study, we propose a machine learning-based earthworm species identification model that uses digital images of earthworms. We performed a stringent performance evaluation not only through 10-fold cross-validation and on an external validation dataset but also in real settings by involving an experienced taxonomist. In all the evaluation settings, our proposed model has given state-of-the-art performance and justified its use to aid earthworm taxonomy studies. We made this model openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/ESIDE., Competing Interests: The authors have declared that no competing interests exist.

In the dairy farming industry, we can obtain the temperature, color, and location information of dairy cows by patrol inspection robot so as to monitor the health status and abnormal behaviors of dairy cows. We build and calibrate a heterogeneous binocular stereo vision (HBSV) system comprising a high-definition color camera and infrared thermal camera and mount it on a patrol inspection robot. First, based on the traditional chessboard, an easy-to-make calibration board for the HBSV system is designed. Second, an accurate locating and sorting algorithm for the calibration points of the calibration board is designed. Then, the cameras are calibrated and the HBSV system is stereo-calibrated. Finally, target locating is achieved based on the above calibration results and Yolo target detection technology. In this paper, several experiments are carried out from many aspects. The target locating average error of HBSV system is 3.11%, which satisfies the needs of the dairy farming environment. The video's FPS captured by using HBSV is 7.3, which is 78% higher than that by using binocular stereo vision system and infrared thermal camera. The results show that the HBSV system has application value to a certain degree.

Human task performance studies are commonly used for detecting and identifying potential military threats. In this work, these principles are applied to detection of an environmental threat: the invasive Burmese python. A qualitative detection of Burmese pythons with a visible light camera and an 850 nm near-infrared (NIR) camera was performed in natural Florida backgrounds. The results showed that the difference in reflectivity between the pythons and native foliage was much greater in NIR, effectively circumventing the python's natural camouflage in the visible band. In this work, a comparison of detection performance in the selected near-infrared band versus the visible band was conducted. Images of foliage backgrounds with and without a python were taken in each band in daylight and at night with illumination. Intensities of these images were then calibrated and prepared for a human perception test. Participants were tasked with detecting pythons, and the human perception data was used to compare performance between the bands. The results show that the enhanced contrast in the NIR enabled participants to detect pythons at 20% longer ranges than the use of visible imagery.

Spectral response (or sensitivity) functions of a three-color image sensor (or trichromatic camera) allow a mapping from spectral stimuli to RGB color values. Like biological photosensors, digital RGB spectral responses are device dependent and significantly vary from model to model. Thus, the information on the RGB spectral response functions of a specific device is vital in a variety of computer vision as well as mobile health (mHealth) applications. Theoretically, spectral response functions can directly be measured with sophisticated calibration equipment in a specialized laboratory setting, which is not easily accessible for most application developers. As a result, several mathematical methods have been proposed relying on standard color references. Typical optimization frameworks with constraints are often complicated, requiring a large number of colors. We report a compressive sensing framework in the frequency domain for accurately predicting RGB spectral response functions only with several primary colors. Using a scientific camera, we first validate the estimation method with direct spectral sensitivity measurements and ensure that the root mean square errors between the ground truth and recovered RGB spectral response functions are negligible. We further recover the RGB spectral response functions of smartphones and validate with an expanded color checker reference. We expect that this simple yet reliable estimation method of RGB spectral sensitivity can easily be applied for color calibration and standardization in machine vision, hyperspectral filters, and mHealth applications that capitalize on the built-in cameras of smartphones.

The purpose of this work is to provide an effective social distance monitoring solution in low light environments in a pandemic situation. The raging coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has brought a global crisis with its deadly spread all over the world. In the absence of an effective treatment and vaccine the efforts to control this pandemic strictly rely on personal preventive actions, e.g., handwashing, face mask usage, environmental cleaning, and most importantly on social distancing which is the only expedient approach to cope with this situation. Low light environments can become a problem in the spread of disease because of people's night gatherings. Especially, in summers when the global temperature is at its peak, the situation can become more critical. Mostly, in cities where people have congested homes and no proper air cross-system is available. So, they find ways to get out of their homes with their families during the night to take fresh air. In such a situation, it is necessary to take effective measures to monitor the safety distance criteria to avoid more positive cases and to control the death toll. In this paper, a deep learning-based solution is proposed for the above-stated problem. The proposed framework utilizes the you only look once v4 (YOLO v4) model for real-time object detection and the social distance measuring approach is introduced with a single motionless time of flight (ToF) camera. The risk factor is indicated based on the calculated distance and safety distance violations are highlighted. Experimental results show that the proposed model exhibits good performance with 97.84% mean average precision (mAP) score and the observed mean absolute error (MAE) between actual and measured social distance values is 1.01 cm., Competing Interests: The authors have declared that no competing interests exist.

Near-infrared (NIR) fluorescence-guided surgery is an innovative technique for the real-time visualization of resection margins. The aim of this study was to develop a head and neck multicellular tumor spheroid model and to explore the possibilities offered by it for the evaluation of cameras for NIR fluorescence-guided surgery protocols. FaDu spheroids were incubated with indocyanine green (ICG) and then included in a tissue-like phantom. To assess the capability of Fluobeam® NIR camera to detect ICG in tissues, FaDu spheroids exposed to ICG were embedded in 2, 5 or 8 mm of tissue-like phantom. The fluorescence signal was significantly higher between 2, 5 and 8 mm of depth for spheroids treated with more than 5 µg/mL ICG ( p < 0.05). The fluorescence intensity positively correlated with the size of spheroids ( p < 0.01), while the correlation with depth in the tissue-like phantom was strongly negative ( p < 0.001). This multicellular spheroid model embedded in a tissue-like phantom seems to be a simple and reproducible in vitro tumor model, allowing a comparison of NIR cameras. The ideal configuration seems to be 450 μm FaDu spheroids incubated for 24 hours with 0.05 mg/ml of ICG, ensuring the best stability, toxicity, incorporation and signal intensity.

Fundus examination plays an important part in a medical setting. The fundus camera is one of the detection instruments used in obtaining fundus images, which can reflect information about disease and other conditions. However, traditional fundus cameras have many disadvantages in regard to data sharing, image recognition, and processing, as well as doctor-patient communication. In recent years, mobile medical systems have gradually become more prevalent in medical and health system environments. In this paper, we propose a design method for a smartphone fundus camera consisting of an illumination system and an imaging system. The end of the system can be combined with a smartphone to take the fundus images directly. We manufactured a prototype, designed an artificial eye model, and carried out a series of experiments. The results show that we can get fundus images clearly, and the imaging system will be able to correct refractive errors ranging from -8 D ∼+8 D . The spatial resolution of the system is up to 15 µm. This is a portable device with an overall size of 160 m m ×160 m m ×80 m m and a weight of 540 g. It has the advantages of lower price, simple operation, high resolution, and compact size, making it suitable as a portable ocular monitoring device.

Purpose: Diabetic retinopathy (DR) is the leading cause of vision loss among active adults in industrialized countries. We aimed to investigate the prevalence of diabetes mellitus (DM), DR and its different grades, in patients with DM in the Csongrád County, South-Eastern region, Hungary. Furthermore, we aimed to detect the risk factors for developing DR and the diabetology/ophthalmology screening patterns and frequencies, as well as the effect of socioeconomic status- (SES-) related factors on the health and behavior of DM patients., Methods: A cross-sectional study was conducted on adults (>18 years) involving handheld fundus camera screening (Smartscope Pro Optomed, Finland) and image assessment using the Spectra DR software (Health Intelligence, England). Self-completed questionnaires on self-perceived health status (SPHS) and health behavior, as well as visual acuity, HbA1c level, type of DM, and attendance at healthcare services were also recorded., Results: 787 participants with fundus camera images and full self-administered questionnaires were included in the study; 46.2% of the images were unassessable. T1D and T2D were present in 13.5% and 86.5% of the participants, respectively. Among the T1D and T2D patients, 25.0% and 33.5% had DR, respectively. The SES showed significant proportion differences in the T1D group. Lower education was associated with a lower DR rate compared to non-DR (7.7% vs. 40.5%), while bad/very bad perceived financial status was associated with significantly higher DR proportion compared to non-DR (63.6% vs. 22.2%). Neither the SPHS nor the health behavior showed a significant relationship with the disease for both DM groups. Mild nonproliferative retinopathy without maculopathy (R1M0) was detected in 6% and 23% of the T1D and T2D patients having DR, respectively; R1 with maculopathy (R1M1) was present in 82% and 66% of the T1D and T2D groups, respectively. Both moderate nonproliferative retinopathy with maculopathy (R2M1) and active proliferative retinopathy with maculopathy (R3M1) were detected in 6% and 7% of the T1D and T2D patients having DR, respectively. The level of HbA1c affected the attendance at the diabetology screening (HbA1c > 7% associated with >50% of all quarter-yearly attendance in DM patients, and with 10% of the diabetology screening nonattendance)., Conclusion: The prevalence of DM and DR in the studied population in Hungary followed the country trend, with a slightly higher sight-threatening DR than the previously reported national average. SES appears to affect the DR rate, in particular, for T1D. Although DR screening using handheld cameras seems to be simple and dynamic, much training and experience, as well as overcoming the issue of decreased optic clarity is needed to achieve a proper level of image assessability, and in particular, for use in future telemedicine or artificial intelligence screening programs., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Dóra Júlia Eszes et al.)

We report an ultrathin arrayed camera (UAC) for high-contrast near infrared (NIR) imaging by using microlens arrays with a multilayered light absorber. The UAC consists of a multilayered composite light absorber, inverted microlenses, gap-alumina spacers and a planar CMOS image sensor. The multilayered light absorber was fabricated through lift-off and repeated photolithography processes. The experimental results demonstrate that the image contrast is increased by 4.48 times and the MTF 50 is increased by 2.03 times by eliminating optical noise between microlenses through the light absorber. The NIR imaging of UAC successfully allows distinguishing the security strip of authentic bill and the blood vessel of finger. The ultrathin camera offers a new route for diverse applications in biometric, surveillance, and biomedical imaging.

The tri-spine horseshoe crab, Tachypleus tridentatus, is a threatened species that inhabits coastal areas from South to East Asia. A Conservation management system is urgently required for managing its nursery habitats, i.e., intertidal flats, especially in Japan. Habitat suitability maps are useful in drafting conservation plans; however, they have rarely been prepared for juvenile T. tridentatus. In this study, we examined the possibility of constructing robust habitat suitability models (HSMs) for juveniles based on topographical data acquired using unmanned aerial vehicles and the Structure from Motion (UAV-SfM) technique. The distribution data of the juveniles in the Tsuyazaki and Imazu intertidal flats from 2017 to 2019 were determined. The data were divided into a training dataset for HSM construction and three test datasets for model evaluation. High accuracy digital surface models were built for each region using the UAV-SfM technique. Normalized elevation was assessed by converting the topographical models that consider the tidal range in each region, and the slope was calculated based on these models. Using the training data, HSMs of the juveniles were constructed with normalized elevation and slope as the predictor variables. The HSMs were evaluated using the test data. The results showed that HSMs exhibited acceptable discrimination performance for each region. Habitat suitability maps were built for the juveniles in each region, and the suitable areas were estimated to be approximately 6.1 ha of the total 19.5 ha in Tuyazaki, and 3.7 ha of the total 7.9 ha area in Imazu. In conclusion, our findings support the usefulness of the UAV-SfM technique in constructing HSMs for juvenile T. tridentatus. The monitoring of suitable habitat areas for the juveniles using the UAV-SfM technique is expected to reduce survey costs, as it can be conducted with fewer investigators over vast intertidal zones within a short period of time., Competing Interests: The authors have read the journal’s policy and have the following potential competing interests: HK is a paid employee of Arithmer Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.