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Task-based functional MRI (tb-fMRI) represents an extremely valuable approach for the identification of language eloquent regions for presurgical mapping in patients with brain tumors. However, its routinely application is limited by patient-related factors, such as cognitive disability and difficulty in coping with long-time acquisitions, and by technical factors, such as lack of equipment availability for stimuli delivery. Resting-state fMRI (rs-fMRI) instead, allows the identification of distinct language networks in a 10-min acquisition without the need of performing active tasks and using specific equipment. Therefore, to test the feasibility of rs-fMRI as a preoperative mapping tool, we reconstructed a lexico-semantic intrinsic connectivity network (ICN) in healthy controls (HC) and in a case series of patients with gliomas and compared the organization of this language network with the one derived from tb-fMRI in the patient's group. We studied three patients with extra-frontal gliomas who underwent functional mapping with auditory verb-generation (AVG) task and rs-fMRI with a seed in the left inferior frontal gyrus (IFG). First, we identified the functional connected areas to the IFG in HC. We qualitatively compared these areas with those that showed functional activation in AVG task derived from Neurosynth meta-analysis. Last, in each patient we performed single-subject analyses both for rs- and tb-fMRI, and we evaluated the spatial overlap between the two approaches. In HC, the IFG-ICN network showed a predominant left fronto-temporal functional connectivity in regions overlapping with the AVG network derived from a meta-analysis. In two patients, rs- and tb-fMRI showed comparable patterns of activation in left fronto-temporal regions, with different levels of contralateral activations. The third patient could not accomplish the AVG task and thus it was not possible to make any comparison with the ICN. However, in this patient, task-free approach disclosed a consistent network of fronto-temporal regions as in HC, and additional parietal regions. Our preliminary findings support the value of rs-fMRI approach for presurgical mapping, particularly for identifying left fronto-temporal core language-related areas in glioma patients. In a preoperative setting, rs-fMRI approach could represent a powerful tool for the identification of eloquent language areas, especially in patients with language or cognitive impairments.

Laser-based mid-infrared (mid-IR) photothermal spectroscopy (PTS) represents a selective, fast, and sensitive analytical technique. Recent developments in laser design permits the coverage of wider spectral regions in combination with higher power, enabling for qualitative reconstruction of broadband absorption features, typical of liquid or solid samples. In this work, we use an external cavity quantum cascade laser (EC-QCL) that emits in pulsed mode in the region between 5.7 and 6.4 µm (1770–1560 cm−1), to measure the absorption spectrum of a thin film of polymethyl methacrylate (PMMA) spin-coated on top of a silicon nitride (Si3N4) micro-ring resonator (MRR). Being the PTS signal inversely proportional to the volume of interaction, in the classical probe–pump dual beam detection scheme, we exploit a Si3N4 transducer coated with PMMA, as a proof-of-principle for an on-chip photothermal sensor. By tuning the probe laser at the inflection point of one resonance, aiming for highest sensitivity, we align the mid-IR beam on top of the ring’s area, in a transversal configuration. To maximize the amplitude of the photoinduced thermal change, we focus the mid-IR light on top of the ring using a Cassegrain reflector enabling for an optimal match between ring size and beam waist of the excitation source. We briefly describe the transducer design and fabrication process, present the experimental setup, and perform an analysis for optimal operational parameters. We comment on the obtained results showing that PTS allows for miniaturized robust sensors opening the path for on-line/in-line monitoring in several industrial processes.

Diagnosing atypical pigmented facial lesions (aPFLs) is a challenging topic for dermatologists. Accurate diagnosis of these lesions is crucial for effective patient management, especially in dermatology, where visual assessment plays a central role. Incorrect diagnoses can result in mismanagement, delays in appropriate interventions, and potential harm. AI, however, holds the potential to enhance diagnostic accuracy and provide reliable support to clinicians. This work aimed to evaluate and compare the effectiveness of machine learning (logistic regression of lesion features and patient metadata) and deep learning (CNN analysis of images) models in dermoscopy diagnosis and the management of aPFLs. This study involved the analysis of 1197 dermoscopic images of facial lesions excised due to suspicious and histologically confirmed malignancy, classified into seven classes (lentigo maligna—LM; lentigo maligna melanoma—LMM; atypical nevi—AN; pigmented actinic keratosis—PAK; solar lentigo—SL; seborrheic keratosis—SK; and seborrheic lichenoid keratosis—SLK). Image samples were collected through the Integrated Dermoscopy Score (iDScore) project. The statistical analysis of the dataset shows that the patients mean age was 65.5 ± 14.2, and the gender was equally distributed (580 males—48.5%; 617 females—51.5%). A total of 41.7% of the sample constituted malignant lesions (LM and LMM). Meanwhile, the benign lesions were mainly PAK (19.3%), followed by SL (22.2%), AN (10.4%), SK (4.0%), and SLK (2.3%). The lesions were mainly localised in the cheek and nose areas. A stratified analysis of the assessment provided by the enrolled dermatologists was also performed, resulting in 2445 evaluations of the 1197 images (2.1 evaluations per image on average). The physicians demonstrated higher accuracy in differentiating between malignant and benign lesions (71.2%) than in distinguishing between the seven specific diagnoses across all the images (42.9%). The logistic regression model obtained a precision of 39.1%, a sensitivity of 100%, a specificity of 33.9%, and an accuracy of 53.6% on the test set, while the CNN model showed lower sensitivity (58.2%) and higher precision (47.0%), specificity (90.8%), and accuracy (59.5%) for melanoma diagnosis. This research demonstrates how AI can enhance the diagnostic accuracy in complex dermatological cases like aPFLs by integrating AI models with clinical data and evaluating different diagnostic approaches, paving the way for more precise and scalable AI applications in dermatology, showing their critical role in improving patient management and the outcomes in dermatology.

BACKGROUND: Coronary obstruction following transcatheter aortic valve replacement (TAVR) is a life-threatening complication. For patients at elevated risk, it is not known how valve choice is influenced by clinical and anatomic factors and how outcomes differ between valve platforms. For patients at high risk of coronary obstruction, we sought to describe the anatomical and clinical characteristics of patients treated with both balloon-expandable (BE) and self-expanding (SE) valves. METHODS: This was a multicenter international registry of patients undergoing TAVR who are considered to be at high risk of coronary obstruction and receiving pre-emptive coronary protection. RESULTS: A total of 236 patients were included. Patients receiving SE valves were more likely to undergo valve-in-valve procedures and also had smaller sinuses of Valsalva and valve-to-coronary distance. Three-year cardiac mortality was 21.6% with SE vs 3.7% with BE valves. This was primarily driven by increased rates of definite or probable coronary occlusion, which occurred in 12.1% of patients with SE valves vs 2.1% in patients with BE valves. CONCLUSIONS: In patients undergoing TAVR with coronary protection, those treated with SE valves had increased rates of clinical and anatomic features that increase the risk of coronary obstruction. These include an increased frequency of valve-in-valve procedures, smaller sinuses of Valsalva, and smaller valve-to-coronary distances. These patients were observed to have increased cardiac mortality compared with patients treated with BE valves, but this is likely due to their higher risk clinical and anatomic phenotypes rather than as a function of the valve type itself.

SU-8 is an emerging polymer material for integrated optical circuits that has demonstrated good structural properties in a cryogenic environment. In this article, we investigate the thermo-optical properties of SU-8 for a wavelength λ=850 nm, from room temperature to cryogenic temperature down to 14 K. To measure the material properties, we designed and fabricated SU-8 racetrack resonators via electron beam lithography. While cooling the device in a closed-cycle cryostat, we measured the resonance spectrum as a function of the temperature from which we determined the temperature-induced variations of the group and effective indices of the waveguide. With the aid of waveguide eigenmode simulations, we used these data to derive the temperature dependence of the SU-8 refractive index nSU−8T. At room temperature (T~295 K), the thermo-optic coefficient dnSU−8/dT=−5.3±0.2×10−5 K−1. At low temperature (T~14 K), dnSU−8/dT=−1.27±0.05×10−4 K−1. Our research shows the potential of SU-8 photonics in a cryogenic environment, suitable for the integration with quantum light sources emitting in the near infrared (NIR).

There has been growing scientific interest in the research field of deep learning techniques applied to skin cancer diagnosis in the last decade. Though encouraging data have been globally reported, several discrepancies have been observed in terms of study methodology, result presentations and validation in clinical settings. The present review aimed to screen the scientific literature on the application of DL techniques to dermoscopic melanoma/nevi differential diagnosis and extrapolate those original studies adequately by reporting on a DL model, comparing them among clinicians and/or another DL architecture. The second aim was to examine those studies together according to a standard set of statistical measures, and the third was to provide dermatologists with a comprehensive explanation and definition of the most used artificial intelligence (AI) terms to better/further understand the scientific literature on this topic and, in parallel, to be updated on the newest applications in the medical dermatologic field, along with a historical perspective. After screening nearly 2000 records, a subset of 54 was selected. Comparing the 20 studies reporting on convolutional neural network (CNN)/deep convolutional neural network (DCNN) models, we have a scenario of highly performant DL algorithms, especially in terms of low false positive results, with average values of accuracy (83.99%), sensitivity (77.74%), and specificity (80.61%). Looking at the comparison with diagnoses by clinicians (13 studies), the main difference relies on the specificity values, with a +15.63% increase for the CNN/DCNN models (average specificity of 84.87%) compared to humans (average specificity of 64.24%) with a 14,85% gap in average accuracy; the sensitivity values were comparable (79.77% for DL and 79.78% for humans). To obtain higher diagnostic accuracy and feasibility in clinical practice, rather than in experimental retrospective settings, future DL models should be based on a large dataset integrating dermoscopic images with relevant clinical and anamnestic data that is prospectively tested and adequately compared with physicians.

The OHIO (Odin Hospital Indoor cOmpass) project received funding from the European Union’s Horizon 2020 research and innovation action program, via ODIN–Open Call, which is issued and executed under the ODIN project and focuses on enhancing hospital safety, productivity, and quality by introducing digital solutions, such as the Internet of Things (IoT), robotics, and artificial intelligence (AI). OHIO aims to enhance the productivity and quality of medical equipment maintenance activities within the pilot hospital, “Le Scotte” in Siena (Italy), by leveraging internal informational resources. OHIO will also be completely integrated with the ODIN platform, taking advantage of the available services and functionalities. OHIO exploits Bluetooth Low Energy (BLE) tags and antennas together with the resources provided by the ODIN platform to develop a complex ontology-based IoT framework, which acts as a central cockpit for the maintenance of medical equipment through a central management web application and an indoor real-time location system (RTLS) for mobile devices. The application programmable interfaces (APIs) are based on REST architecture for seamless data exchange and integration with the hospital’s existing computer-aided facility management (CAFM) and computerized maintenance management system (CMMS) software. The outcomes of the project are assessed both with quantitative and qualitative methods, by evaluating key performance indicators (KPIs) extracted from the literature and performing a preliminary usability test on both the whole system and the graphic user interfaces (GUIs) of the developed applications. The test implementation demonstrates improvements in maintenance timings, including a reduction in maintenance operation delays, duration of maintenance tasks, and equipment downtime. Usability post-test questionnaires show positive feedback regarding the usability and effectiveness of the applications. The OHIO framework enhanced the effectiveness of medical equipment maintenance by integrating existing software with newly designed, enhanced interfaces. The research also indicates possibilities for scaling up the developed methods and applications to additional large-scale pilot hospitals within the ODIN network.

Integrated photonics on Silicon-On-Insulator (SOI) substrates is a well developed research field that has already significantly impacted various fields, such as quantum computing, micro sensing devices, biosensing, and high-rate communications. Although quite complex circuits can be made with such technology, everything is based on a few ’building blocks’ which are then combined to form more complex circuits. This review article provides a detailed examination of the state of the art of integrated photonic building blocks focusing on passive elements, covering fundamental principles and design methodologies. Key components discussed include waveguides, fiber-to-chip couplers, edges and gratings, phase shifters, splitters and switches (including y-branch, MMI, and directional couplers), as well as subwavelength grating structures and ring resonators. Additionally, this review addresses challenges and future prospects in advancing integrated photonic circuits on SOI platforms, focusing on scalability, power efficiency, and fabrication issues. The objective of this review is to equip researchers and engineers in the field with a comprehensive understanding of the current landscape and future trajectories of integrated photonic components on SOI substrates with a 220 nm thick device layer of intrinsic silicon.

Integrated photonic platforms have rapidly emerged as highly promising and extensively investigated systems for advancing classical and quantum information technologies, since their ability to seamlessly integrate photonic components within the telecommunication band with existing silicon-based industrial processes offers significant advantages. However, despite this integration facilitating the development of novel devices, fostering fast and reliable communication protocols and the manipulation of quantum information, traditional integrated silicon photonics faces inherent physical limitations that necessitate a challenging trade-off between device efficiency and spatial footprint. To address this issue, researchers are focusing on the integration of nanoscale materials into photonic platforms, offering a novel approach to enhance device performance while reducing spatial requirements. These developments are of paramount importance in both classical and quantum information technologies, potentially revolutionizing the industry. In this review, we explore the latest endeavors in hybrid photonic platforms leveraging the combination of integrated silicon photonic platforms and nanoscale materials, allowing for the unlocking of increased device efficiency and compact form factors. Finally, we provide insights into future developments and the evolving landscape of hybrid integrated photonic nanomaterial platforms.

The primary goal of this project is to create a framework to extract Real-World Evidence to support Health Technology Assessment, Health Technology Management, Evidence-Based Maintenance, and Post Market Surveillance (as outlined in the EU Medical Device Regulation 2017/745) of medical devices using Natural Language Processing (NLP) and Artificial Intelligence. An initial literature review on Spontaneous Reporting System databases, Health Information Technologies (HIT) fault classification, and Natural Language Processing has been conducted, from which it clearly emerges that adverse events related to HIT are increasing over time. The proposed framework uses NLP techniques and Explainable Artificial Intelligence models to automatically identify HIT-related adverse event reports. The designed model employs a pre-trained version of ClinicalBERT that has been fine-tuned and tested on 3,075 adverse event reports extracted from the FDA MAUDE database and manually labelled by experts.

BackgroundMR Tractography enables non-invasive preoperative depiction of language subcortical tracts, which is crucial for the presurgical work-up of brain tumors; however, it cannot evaluate the exact function of the fibers.PurposeA systematic pipeline was developed to combine tractography reconstruction of language fiber bundles, based on anatomical landmarks (Anatomical-T), with language fMRI cortical activations. A fMRI-targeted Tractography (fMRI-T) was thus obtained, depicting the subsets of the anatomical tracts whose endpoints are located inside a fMRI activation. We hypothesized that fMRI-T could provide additional functional information regarding the subcortical structures, better reflecting the eloquent white matter structures identified intraoperatively.MethodsBoth Anatomical-T and fMRI-T of language fiber tracts were performed on 16 controls and preoperatively on 16 patients with left-hemisphere brain tumors, using a q-ball residual bootstrap algorithm based on High Angular Resolution Diffusion Imaging (HARDI) datasets (b = 3000 s/mm2; 60 directions); fMRI ROIs were obtained using picture naming, verbal fluency, and auditory verb generation tasks. In healthy controls, normalized MNI atlases of fMRI-T and Anatomical-T were obtained. In patients, the surgical resection of the tumor was pursued by identifying eloquent structures with intraoperative direct electrical stimulation mapping and extending surgery to the functional boundaries. Post-surgical MRI allowed to identify Anatomical-T and fMRI-T non-eloquent portions removed during the procedure.ResultsMNI Atlases showed that fMRI-T is a subset of Anatomical-T, and that different task-specific fMRI-T involve both shared subsets and task-specific subsets - e.g., verbal fluency fMRI-T strongly involves dorsal frontal tracts, consistently with the phonogical-articulatory features of this task. A quantitative analysis in patients revealed that Anatomical-T removed portions of AF-SLF and IFOF were significantly greater than verbal fluency fMRI-T ones, suggesting that fMRI-T is a more specific approach. In addition, qualitative analyses showed that fMRI-T AF-SLF and IFOF predict the exact functional limits of resection with increased specificity when compared to Anatomical-T counterparts, especially the superior frontal portion of IFOF, in a subcohort of patients.ConclusionThese results suggest that performing fMRI-T in addition to the 'classic' Anatomical-T may be useful in a preoperative setting to identify the 'high-risk subsets' that should be spared during the surgical procedure.