Although computer-aided diagnosis (CADx) and detection (CADe) systems have made significant progress in various medical domains, their application is still limited in specialized fields such as otorhinolaryngology. In the latter, current assessment methods heavily depend on operator expertise, and the high heterogeneity of lesions complicates diagnosis, with biopsy persisting as the gold standard despite its substantial costs and risks. A critical bottleneck for specialized endoscopic CADx/e systems is the lack of well-annotated datasets with sufficient variability for real-world generalization. This study introduces a novel approach that exploits a Latent Diffusion Model (LDM) coupled with a ControlNet adapter to generate laryngeal endoscopic image-annotation pairs, guided by clinical observations. The method addresses data scarcity by conditioning the diffusion process to produce realistic, high-quality, and clinically relevant image features that capture diverse anatomical conditions. The proposed approach can be leveraged to expand training datasets for CADx/e models, empowering the assessment process in laryngology. Indeed, during a downstream task of detection, the addition of only 10 https://github.com/ChiaraBaldini/endoLDMC.git ).

PURPOSE:Conventionally, the treatment of carotid artery disease customizes stents to the vessel diameter. The design of the CGuard stent and its nitinol shape memory allow for a wide operational range and minimal residual radial force. This pilot study assesses the feasibility and clinical outcomes of the 10-mm CGuard stent in a one-size-fits-all sizes protocol. MATERIALS AND METHODS:The study is a multicenter, prospective cohort study involving 226 consecutive patients with symptomatic or asymptomatic carotid artery stenosis, with an indication for revascularization. All patients received the 10-mm CGuard stent, irrespective of the carotid artery reference diameter, and were grouped into three categories based on the reference vessel diameter. Endpoints included procedural success and incidence of major adverse cardiovascular events (MACE) at 30 days and 1 year. RESULTS:The study achieved 100% technical success, with an average residual stenosis of 8.41%. Clinical success was 98.7%. During in-hospital observation, one patient experienced a major ipsilateral stroke (0.4%), and two patients had transient ischemic attacks (TIAs) (0.8%). At the 30-day follow-up, there were one death and one case of re-occlusion which was asymptomatic. No differences between the three groups were found regarding technical or procedural success, residual stenosis, complications, TIAs, MACEs, or patency rates. No events occurred between the 30-day and 1-year follow-up, maintaining the MACE rate at 1.5%. ICA and ECA patency rates at 1 year were 99% and 99.4%, respectively, indicating mid-term treatment effectiveness. CONCLUSION:This pilot study demonstrates that the one-size-fits-all approach using a 10-mm CGuard stent to treat carotid artery stenosis provides minimal residual radial force with optimal stent apposition. The one-size-fits-all approach with CGuard stent is feasible, safe, and effective. Further studies for confirmation are guaranteed.

BackgroundIn mechanical thrombectomy (MT), extracranial vascular tortuosity is among the main determinants of procedure duration and success. Currently, no rapid and reliable method exists to identify the anatomical features precluding fast and stable access to the cervical vessels.MethodsA retrospective sample of 513 patients were included in this study. Patients underwent first-line transfemoral MT following anterior circulation large vessel occlusion stroke. Difficult transfemoral access (DTFA) was defined as impossible common carotid catheterization or time from groin puncture to first carotid angiogram >30 min. A machine learning model based on 29 anatomical features automatically extracted from head-and-neck computed tomography angiography (CTA) was developed to predict DTFA. Three experienced raters independently assessed the likelihood of DTFA on a reduced cohort of 116 cases using a Likert scale as benchmark for the model, using preprocedural CTA as well as automatic 3D vascular segmentation separately.ResultsAmong the study population, 11.5% of procedures (59/513) presented DTFA. Six different features from the aortic, supra-aortic, and cervical regions were included in the model. Cross-validation resulted in an area under the receiver operating characteristic (AUROC) curve of 0.76 (95% CI 0.75 to 0.76) for DTFA prediction, with high sensitivity for impossible access identification (0.90, 95% CI 0.81 to 0.94). The model outperformed human assessment in the reduced cohort [F1-score (95% CI) by experts with CTA: 0.43 (0.37 to 0.50); experts with 3D segmentation: 0.50 (0.46 to 0.54); and model: 0.70 (0.65 to 0.75)].ConclusionsA fully automatic model for DTFA prediction was developed and validated. The presented method improved expert assessment of difficult access prediction in stroke MT. Derived information could be used to guide decisions regarding arterial access for MT.

BACKGROUND:Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation therapy (3D-CRT) remains pivotal, influenced by patient anatomy and dosimetric constraints. PURPOSE:This study aims to develop a decision-making framework utilizing deep learning to predict dose distributions, aiding in the selection of optimal treatment techniques. METHODS:A 2D U-Net convolutional neural network (CNN) model was used to predict dose distribution maps and dose-volume histogram (DVH) metrics for breast cancer patients undergoing IMRT and 3D-CRT. The model was trained and fine-tuned using retrospective datasets from two medical centers, accounting for variations in CT systems, dosimetric protocols, and clinical practices, over 346 patients. An additional 30 consecutive patients were selected for external validation, where both 3D-CRT and IMRT plans were manually created. To show the potential of the approach, an independent medical physicist evaluated both dosimetric plans and selected the most appropriate one based on applicable clinical criteria. Confusion matrices were used to compare the decisions of the independent observer with the historical decision and the proposed decision-making framework. RESULTS:Evaluation metrics, including dice similarity coefficients (DSC) and DVH analyses, demonstrated high concordance between predicted and clinical dose distribution for both IMRT and 3D-CRT techniques, especially for organs at risk (OARs). The decision-making framework demonstrated high accuracy (90 % $\%$ ), recall (95.7 % $\%$ ), and precision (91.7 % $\%$ ) when compared to independent clinical evaluations, while the historical decision-making had lower accuracy (50 % $\%$ ), recall (47.8 % $\%$ ), and precision (78.6 % $\%$ ). CONCLUSIONS:The proposed decision-making model accurately predicts dose distributions for both 3D-CRT and IMRT, ensuring reliable OAR dose estimation. This decision-making framework significantly outperforms historical decision-making, demonstrating higher accuracy, recall, and precision.

Background and Objective: Oropharynx Squamous Cell Carcinoma (OPSCC) linked to Human Papillomavirus (HPV) exhibits a more favorable prognosis than other squamous cell carcinomas of the upper aerodigestive tract. Finding reliable non-invasive detection methods of this prognostic entity is key to propose appropriate therapeutic decisions. This study aims to provide a comprehensive method based on pre-treatment clinical data for predicting the patient’s HPV status over a large OPSCC patient cohort and employing explainability techniques to interpret the significance and effects of the features. Materials and Methods: We employed the RADCURE dataset clinical information to train six Machine Learning algorithms, evaluating them via cross-validation for grid search hyper-parameter tuning and feature selection as well as a final performance measurement on a 20% sample test set. For explainability, SHAP and LIME were used to identify the most relevant relationships and their effect on the predictive model. Furthermore, additional publicly available datasets were scrutinized to compare outcomes and assess the method’s generalization across diverse feature sets and populations. Results: The best model yielded an AUC of 0.85, a sensitivity of 0.83, and a specificity of 0.75 over the testing set. The explainability analysis highlighted the remarkable significance of specific clinical attributes, in particular the oropharynx subsite tumor location and the patient’s smoking history. The contribution of each variable to the prediction was substantiated by creating a 95% confidence intervals of model coefficients by means of a 10,000 sample bootstrap and by analyzing top contributors across the best-performing models. Conclusions: The combination of specific clinical factors typically collected for OPSCC patients, such as smoking habits and the tumor oropharynx sub-location, along with the ML models hereby presented, can by themselves provide an informed analysis of the HPV status, and of proper use of data science techniques to explain it. Future work should focus on adding other data modalities such as CT scans to enhance performance and to uncover new relations, thus aiding medical practitioners in diagnosing OPSCC more accurately.

Background The operator's ability in performing carotid stenting (CAS) has improved clinical outcomes. However, more than 3% of patients need to be treated again after CAS. Most of the cases requiring further intervention are affected by hyperplasia. The recommended procedure is the DEBalloon. On the other hand, the literature reports a small number of carotid neoatherosclerosis cases and is recommended to be treated using elective Micronet-covered stent. Discriminating between the two types of in-stent-restenosis ISR (hyperplasia or neoatherosclerosis) is critical for a positive outcome.Case summary We describe a case in which a patient treated with carotid stenting 8 years before, was diagnosed with ISR. Due to the development of neurological symptoms and progressive increases in Peak Systolic Velocity (PSV) eight years following carotid stenting, a DEBalloon was used in a carotid in-stent-restenosis (ISR) standard procedure. About ten minutes after the procedure, the patient developed hemiplegia consistent with the treated carotid territory. The implantation of a MicroNet-covered stent excluded the thrombus and reverted symptoms with a normal MR control at 24 h. Conclusion This case illustrates that when in-stent stenosis evolves years after the stent implantation, neoatherosclerosis should be assessed, and a MicroNet-covered stent should be considered.

BACKGROUND:In a recent randomized study, MicroNet-covered stent (CGuard) significantly reduced procedural and post-procedural cerebral embolism in relation to a single-layer CREST study carotid stent, but real-life clinical practice data are limited. The aim is to prospectively assess clinical outcomes of CGuard as a routine revascularization tool for patients with indication to carotid revascularization.METHODS:From April 2019 to November 2021, 204 elective patients (age 71.0±7.1years, 69.6% males, 21.7% symptomatic) were enrolled.RESULTS:Mean basal peak-systolic velocity was 251.41±91.85 cm/s with angiographic diameter stenosis 89.7±8.46%. About 34.4% lesions were severely calcified, 6.8% were angulated, and 4.4% showed significant access tortuosity. Access was femoral, with 100% protection device (filter) use. Two hundred and three lesions in 203 patients were treated (1 cross-over to surgery for lack of effective access, no cross-over to other devices); in most cases (66.9%) the stent was placed directly. For pre-dilated lesions, mean balloon diameter was 3.36±0.34mm. Mean nominal stent diameter was 7.64±0.5 mm; length was 37.19±4.5 mm. All stents were post-dilated (balloon diameter 5.2±0.25 mm). Residual stenosis was <30% in all (3.77±6.91%). By discharge, there were 2 minor strokes (0.9%) and one transient ischemic attack. By 30-days, one other minor stroke occurred in relation to de-novo atrial fibrillation. With no deaths or myocardial infarctions, 30-day total death/stroke/myocardial infarction rate was 1.48%. No in-stent thrombosis or patency loss occurred by 30-days. In-stent peak-systolic velocity was 55.49±22.73 cm/s.CONCLUSIONS:Thirty-day results from POLGUARD study indicate safety and a low complication rate of the MicroNet-covered carotid stent use in every-day vascular surgery practice of carotid revascularization. Long-term observation is underway.

Introduction: Vascular tortuosity is considered one of the main barriers for mechanical thrombectomy in stroke. We developed a fully automatic algorithm for vascular tortuosity characterization and explored the association of several anatomical features with baseline characteristics and procedural outcomes. Methods: An automatic software integrating segmentation, centerline extraction and vessel labelling, was used to extract features directly from computed tomography angiograms (CTA) of stroke patients undergoing transfemoral endovascular treatment. Mean diameter and tortuosity index (TI) of the carotid arteries and brachiocephalic trunk were used as relevant tortuosity indicators. Patients were compared according to seven different clinical conditions and four procedural measures. Results: 518 patients (51% female, 74.8±12.9 years) were included in this study. Women presented significantly smaller vessels and increased TI in both carotid arteries ( p <.01). Hypertension and atrial fibrillation were associated with higher TI in both carotid arteries ( p <.01). Age correlated with TI in all vessels (.35< r< .52, p <.01). Patients with longer time to first angiographic run and time to revascularization presented higher TI in all ipsilateral carotid segments ( p <.05). Conclusion: An automated software was successfully used to characterize anatomical vascular features from CTA. Gender, age, hypertension and atrial fibrillation were found to influence vascular tortuosity. The algorithm was able to detect higher TI in ipsilateral carotid arteries of patients with further difficult catheter access or longer time to revascularization. This information could potentially be used to plan best arterial access site.

Introduction Difficult or impossible femoral access remains a significant burden for mechanical thrombectomy (MT) in stroke, affecting between 4.4% (impossible) to ∼10% of cases (time to first angiography [T1A] > 30 min) [1]. Visualization of CT angiography (CTA) or MR angiography are the only available diagnostic tools for neurointerventionalists (NIs) to qualitatively assess catheterization difficulty ahead of intervention. Visualization of 3D segmentation of the head‐and‐neck arteries from CTA may provide enhanced spatial information for catheter access difficulty assessment to the occlusion site, as well as large vessel occlusion (LVO) localization or carotid stenosis detection. Methods Three observers (2 expert NIs and 1 medical image engineer) assessed a sample of patients with anterior circulation stroke rating LVO location, femoral access difficulty according to a Likert scale (0 to 5) and assessment of radial access compared to femoral (easier or not). Raters were asked to answer the same form for each case twice, once after visualizing only the CTA and again after visualizing a deep‐learning, automated 3D segmentation of the head‐and‐neck arteries from CTA [2]. Cases were randomly sampled and blind to raters for both assessments. Likert scale values were normalized and averaged across observers, and correlation to T1A was studied. LVO localization accuracy and time needed for the analysis was also assessed. Results The final sample included N = 117 cases, where 22.98% presented difficult or impossible access. LVO location prevalence was 13/15/45/27% for extracranial ICA (eICA)/TICA/M1/M2, and 49/51% for right/left. Averaged Likert values presented significant linear correlation for both segmentation and CTA visualization, but this was much stronger in the former (R: 0.58 vs 0.30, p<0.001). Three‐dimensional visualization of segmented vessels allowed for an enhanced detection of difficult or impossible femoral access (ROC‐AUC: 0.85 vs 0.66). LVO detection was also superior after segmentation visualization (mean accuracy across observers: 0.81 vs 0.72) and time needed for assessment was on average 2.3 times shorter (44 s vs 100 s). In difficult or impossible patients, radial access was considered easier in 74% (63‐89%) of cases when visualizing the segmentation compared to 42% (35‐48%) when inspecting the CTA. Conclusion Visualization of deep‐learning 3D arterial segmentation resulted in enhanced difficult or impossible femoral access for MT, LVO detection and faster analysis over traditional pre‐procedural CTA inspection. Adding this visualization to the current workflow could enhance difficult access detection ahead of intervention.

BACKGROUND: This study aims to assess the role and safety of post-dilatation in protected carotid artery stenting (PCAS) using the new MicroNet-covered 2nd-generation stent assessed by cone beam CT scans. METHODS: From March 2020 to March 2022, patients were enrolled in the study according to CT angiography results based on the following criteria: Evidence of 70% to 99% carotid stenosis in asymptomatic patients and 50% to 99% in symptomatic patients, per the NASCET index. Using a FilterWire EZT (Boston Scientific, Natick, MA, USA) embolic protection system (EPS), MicroNet-covered stent PCAS was performed by two interventional radiologists with at least 8 years of experience in endovascular intervention. Each patient underwent post-dilatation following stent placement. Finally, a third radiologist (not participating in the interventional procedures) evaluated the cone beam CT scans and calculated residual stenosis. Major and minor complications were recorded in the 30 days following the procedure. RESULTS: A total of 192 patients (121 male, mean age 73 +/- 10 years) were included in the study, and all patients received post-dilatation following stent implantation. Technical successes were achieved in all procedures. Adverse events noted in this study were limited to periprocedural transient ischemic attacks that occurred in three out of 192 patients (1.6%) and showed a swift complete recovery. The post-dilatation balloon diameters used in the study were: 5.0 mm (30.3%), 5.5 mm (39.3%) and 6 mm (30.3%). Optimized postdilatation resulted in a significant increase in the final luminal area. Similar improvements were observed in all subtypes of plaque. CONCLUSIONS: Post-dilatation in protected CAS is safe and induces a significant improvement in the cross-sectional area regardless of the stenotic plaque.

Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 +/- 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter -rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients.

Highlights Classification of date fruit hardness levels from images. Combination of deep networks and an expert system to yield better accuracies. Abstract. Machine learning techniques have been used in various agricultural applications from farming to post-harvest operations. For some cases, a large amount of data is not available and improving on classification accuracies based on deep networks cannot be an option. Such is the case presented here for sorting date fruits based on their hardness into three classes (soft, semi-hard, hard). The original dataset in this work consists of 1800 monochrome images with 600 images per class obtained from different growing regions in Oman. This is a limited number of examples to consider deep networks. Thus, this work proposes data augmentation based on Generative Adversarial Networks (GAN) to synthetically augment the date fruit images. It incorporates a Densely Connected Convolutional Network (DenseNet) for date fruit classification. The goal is to generate enough images so that DenseNet can successfully classify them. The GAN images help during the training part when a large dataset is used for training. Accuracies of 100%, 100%, and 99% were achieved for the hard, soft, and semi-hard classes by using an expert system in combination with two DenseNet networks, one network trained for classifying hard and soft cases as well as a second network that classifies the three classes. Keywords: Data augmentation, Deep learning, Expert system, Fruit classification, GAN, Neural networks.

Purpose: Carotid revascularization can be associated with modifications of the vascular geometry, which may lead to complications. The changes on the vessel angulation before and after a carotid WallStent (WS) implantation are compared against 2 new dual-layer devices, CGuard (CG) and RoadSaver (RS). Materials and Methods: The study prospectively recruited 217 consecutive patients (112 GC, 73 WS, and 32 RS, respectively). Angiography projections were explored and the one having a higher arterial angle was selected as a basal view. After stent implantation, a stent control angiography was performed selecting the projection having the maximal angle. The same procedure is followed in all the 3 stent types to guarantee comparable conditions. The angulation changes on the stented segments were quantified from both angiographies. The statistical analysis quantitatively compared the pre-and post-angles for the 3 stent types. The results are qualitatively illustrated using boxplots. Finally, the relation between pre- and post-angles measurements is analyzed using linear regression. Results: For CG, no statistical difference in the axial vessel geometry between the basal and postprocedural angles was found. For WS and RS, statistical difference was found between pre- and post-angles. The regression analysis shows that CG induces lower changes from the original curvature with respect to WS and RS. Conclusion: Based on our results, CG determines minor changes over the basal morphology than WS and RS stents. Hence, CG respects better the native vessel anatomy than the other stents. Level of Evidence: Level 4, Case Series.

X-ray crystallography is a powerful method that has significantly contributed to our understanding of the biological function of proteins and other molecules. This method relies on the production of crystals that, however, are usually a bottleneck in the process. For some molecules, no crystallization has been achieved or insufficient crystals were obtained. Some other systems do not crystallize at all, such as nanoparticles which, because of their dimensions, cannot be treated by the usual crystallographic methods. To solve this, whole pair distribution function has been proposed to bridge the gap between Bragg and Debye scattering theories. To execute a fitting, the spectra of several different constructs, composed of millions of particles each, should be computed using a particle–pair or particle–particle (pp) distance algorithm. Using this computation as a test bench for current field-programmable gate array (FPGA) technology, we evaluate how the parallel computation capability of FPGAs can be exploited to reduce the computation time. We present two different solutions to the problem using two state-of-the-art FPGA technologies. In the first one, the main C program uses OmpSs (a high-level programming model developed at the Barcelona Supercomputing Center, that enables task offload to different high-performance computing devices) for task invocation, and kernels are built with OpenCL using reduced data sizes to save transmission time. The second approach uses task and data parallelism to operate on data locally and update data globally in a decoupled task. Benchmarks have been evaluated over an Intel D5005 Programmable Acceleration Card, computing a model of 2 million particles in 81.57 s – 24.5 billion atom pairs per second (bapps)– and over a ZU102 in 115.31 s. In our last test, over an up-to-date Alveo U200 board, the computation lasted for 34.68 s (57.67 bapps). In this study, we analyze the results in relation to the classic terms of speed-up and efficiency and give hints for future improvements focused on reducing the global job time.

Highlights Automatic classification of harvester sounds. Final classification obtained using three convolutional neural networks. The results of the networks were combined via stacking and voting to achieve 100% accuracy. Abstract. The use of deep learning in agricultural tasks has recently become popular. Deep learning networks have been used for analyzing images of crops, identifying paddy areas, distinguishing sick plants from healthy ones, to name a few applications. Besides visual systems, sound analysis of agricultural machinery is a time-sensitive task that can also be incorporated in decision making and can be done with the help of deep learning models. We propose a method to generate spectrogram images from the sound of a harvester and classify them into three working modes in real-time. We used three convolutional neural networks and use the outputs of these networks as inputs to a stacking ensemble method to improve the accuracy of the system. To achieve 100% classification accuracy, a final decision is made by voting based on several consecutive classifications made by the stacking step. We were able to perform classifications in less than 1 s which was the standard to be considered as a safe time for the harvester. Keywords: Convolutional neural networks, Deep learning, Spectrograms, Stacking, Voting.

BACKGROUND:Total Knee Arthroplasty (TKA) is an intervention that can significantly improve the quality of life of patients with advanced knee osteoarthritis. Early start of rehabilitation and its continuation at home once the patient is discharged are key factors for the success of the process.This study aims to assess the effectiveness of a home-based telerehabilitation solution (ReHub) on improving functional capacity and clinical outcomes for patients who underwent TKA. METHODS/DESIGN:The study is a randomized, open-label with blinded outcome assessor, parallel assignment clinical trial with a sample size of 52 patients that is conducted according to the SPIRIT recommendations. After the TKA intervention, the patients are randomly allocated to the control group or the experimental group with a 1:1 ratio. Both groups follow a Fast Track recovery protocol which includes discharge after 2-3 days from surgery, a daily plan of 5 exercises for autonomous rehabilitation and domiciliary visits by a physiotherapist starting approximately 2 weeks after surgery. The experimental group uses the sensor-based telerehabilitation system ReHub to perform the exercises. The primary outcome measure is the range of motion of the knee. Secondary outcomes include physical performance, quality of life, pain intensity, muscle strength, treatment adherence and satisfaction with the ReHub system. The outcomes assessment is performed at hospital discharge (baseline), at stitch removal (2 weeks after baseline) and 2 weeks after stitch removal (4 weeks after baseline).The study conforms to the guidelines of the Declaration of Helsinki and was approved by the hospital's ethics committee. DISCUSSION:The study will address an important gap in the evidence base by reporting the effectiveness of an affordable and low-cost home-based telerehabilitation solution in patients who underwent TKA. ETHICS AND DISSEMINATION:The study was approved by the hospital's ethics committee ("Comité Ético de Investigación Clínica del HCB", reg. HCB/2019/0571). The trial was registred at ClinicalTrials.gov (NCT04155957). The results of this study will be published in peer-reviewed journals as well as national and international conferences. TRIAL REGISTRATION:NCT04155957 (ClinicalTrials.gov). HIGHLIGHTS:Assessing a home-based telerehabilitation solution effectiveness in knee surgery.In situations such as the CoVid-19 pandemic, it is a resolutive intervention method.Telerehabilitation is an alternative to conventional face-to-face physical therapy.This system is far less demanding in terms of human resources.Range of motion assessment is the primary outcome measure.