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Diabetic retinopathy (DR) is a serious retinal disease and is considered as a leading cause of blindness in the world. Ophthalmologists use optical coherence tomography (OCT) and fundus photography for the purpose of assessing the retinal thickness, and structure, in addition to detecting edema, hemorrhage, and scars. Deep learning models are mainly used to analyze OCT or fundus images, extract unique features for each stage of DR and therefore classify images and stage the disease. Throughout this paper, a deep Convolutional Neural Network (CNN) with 18 convolutional layers and 3 fully connected layers is proposed to analyze fundus images and automatically distinguish between controls (i.e. no DR), moderate DR (i.e. a combination of mild and moderate Non Proliferative DR (NPDR)) and severe DR (i.e. a group of severe NPDR, and Proliferative DR (PDR)) with a validation accuracy of 88%-89%, a sensitivity of 87%-89%, a specificity of 94%-95%, and a Quadratic Weighted Kappa Score of 0.91-0.92 when both 5-fold, and 10-fold cross validation methods were used respectively. A prior pre-processing stage was deployed where image resizing and a class-specific data augmentation were used. The proposed approach is considerably accurate in objectively diagnosing and grading diabetic retinopathy, which obviates the need for a retina specialist and expands access to retinal care. This technology enables both early diagnosis and objective tracking of disease progression which may help optimize medical therapy to minimize vision loss.

Hepatocellular carcinoma (HCC) is a common type of liver cancer, with a high mortality rate. Hepatocellular carcinoma is a type of liver cancer that can be effectively managed through early detection and accurate diagnosis, followed by a personalized treatment plan that may include surgical resection, liver transplantation, minimally-invasive techniques, immunotherapy, or targeted therapy depending on the stage and severity of the cancer.

Severe spinal cord injury (SCI) leads to skeletal muscle atrophy and adipose tissue infiltration in the skeletal muscle, which can result in compromised muscle mechanical output and lead to health-related complications. In this study, we developed a novel automatic 3-D approach for volumetric segmentation and quantitative assessment of thigh Magnetic Resonance Imaging (MRI) volumes in individuals with chronic SCI as well as non-disabled individuals. In this framework, subcutaneous adipose tissue, inter-muscular adipose tissue and total muscle tissue are segmented using linear combination of discrete Gaussians algorithm. Also, three thigh muscle groups were segmented utilizing the proposed 3-D Joint Markov Gibbs Random Field model that integrates first order appearance model, spatial information, and shape model to localize the muscle groups. The accuracy of the automatic segmentation method was tested both on SCI (N = 16) and on non-disabled (N = 14) individuals, showing an overall 0.93±0.06 accuracy for adipose tissue and muscle compartments segmentation based on Dice Similarity Coefficient. The proposed framework for muscle compartment segmentation showed an overall higher accuracy compared to ANTs and STAPLE, two previously validated atlas-based segmentation methods. Also, the framework proposed in this study showed similar Dice accuracy and better Hausdorff distance measure to that obtained using DeepMedic Convolutional Neural Network structure, a well-known deep learning network for 3-D medical image segmentation. The automatic segmentation method proposed in this study can provide fast and accurate quantification of adipose and muscle tissues, which have important health and functional implications in the SCI population.

Renal cell carcinoma (RCC) is considered the most aggressive and common form of renal cancer. Therefore, early detection is crucial to ensure appropriate and effective treatment planning. In our study, we propose a novel computer-aided diagnostic (CAD) approach which incorporates a deep learning ensemble to differentiate between five renal tumor subtypes, utilising the modality of contrast-enhanced computed tomography (CE-CT). The addressed renal lesions are malignant tumors (chromophobe RCC (chRCC), papillary RCC (pRCC), and clear cell RCC (ccRCC)) and benign tumors (renal oncocytoma (RO) and angiomyolipoma (AML)). Our study includes 280 patients who underwent renal biopsy, 112 patients were diagnosed with benign tumors and 168 patients were diagnosed with malignant tumors. Specifically, we propose a multi-stage classification pipeline to categorize the five types of renal tumors, and at each stage, we use a novel ensemble system that is composed of three components: (i) taking the average probability of the output of a convolutional neural network (CNN) resulting from multiple images per patient; (ii) applying a long-short term memory (LSTM) followed by a feed-forward neural network on the last dense layer of the CNN; (iii) applying a 1D convolutional encoder succeeded by a feed-forward neural network on the last dense layer of the CNN. Our proposed approach outperformed previous studies in discriminating between benign and malignant tumors, and to the best of our knowledge, our study is the first paper to account for the five mentioned renal tumor subtypes. Our classification accuracies were 96.4%, 100%, 91.2%, 93.8% for the discrimination between benign vs. malignant, AML vs. RO, ccRCC vs. non-ccRCC, and pRCC vs. chRCC, respectively. Our proposed CAD and the obtained results establish the potential of a reliable non-invasive diagnostic utility for renal tumors.

Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient’s chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

Background The adoption of integrated curricula in medical schools has grown globally, emphasizing the need to understand academic medical staff satisfaction as a crucial factor influencing successful implementation. Aim This study aimed to assess satisfaction levels among academic medical staff with integrated medical curricula, identifying socio-demographic and work-related predictors of dissatisfaction. Methods A cross-sectional, online multinational survey was conducted from December 2023 to April 2024 among 525 academic medical staff. A structured, self-reported questionnaire was used to assess satisfaction levels, demographic characteristics, and work-related factors. The data were analyzed using descriptive and inferential statistics, including logistic regression analysis, to identify predictors of dissatisfaction. Results The study revealed a low satisfaction rate (44.2%) among medical staff, with significant dissatisfaction influenced by nationality (Jordanian: OR = 7.74, and Egyptian: OR = 4.68), male gender (OR = 3.27), over ten years of teaching experience in integrated curricula (OR = 4.25), and employment in governmental universities (OR = 4.23). Dissatisfaction was particularly high with aspects such as the integration of basic and clinical sciences, assessment methods, and implementation schedules. Faculty from private universities demonstrated significantly higher satisfaction (65.1%) compared to those in governmental institutions (41.3%). Younger staff < 45 years reported higher satisfaction rates (50.9%) compared to their older counterparts ≥ 45 years (39.1%), reflecting greater adaptability to curriculum changes. Conclusions The findings highlight substantial dissatisfaction among academic medical staff with the design and implementation of integrated curricula, particularly in governmental institutions and among senior faculty. Institutions must address challenges in curriculum design, resource allocation, and faculty support to enhance satisfaction. Revisiting conventional curriculum components or adopting a hybrid approach may help balance innovation with faculty preferences, fostering a more conducive educational environment.

[This corrects the article DOI: 10.1371/journal.pone.0216487.].

The global spread of COVID-19 highlights the urgency of quickly finding drugs and vaccines and suggests that similar challenges will arise in the future. This underscores the need for ongoing efforts to overcome the obstacles involved in the development of potential treatments. Although some progress has been made in the use of Artificial Intelligence (AI) in drug discovery, virologists, pharmaceutical companies, and investors seek more long-term solutions and greater investment in emerging technologies. One potential solution to aid in the drug-development process is to combine the capabilities of the Internet of Medical Things (IoMT), edge computing (EC), and deep learning (DL). Some practical frameworks and techniques utilizing EC, IoMT, and DL have been proposed for the monitoring and tracking of infected individuals or high-risk areas. However, these technologies have not been widely utilized in drug clinical trials. Given the time-consuming nature of traditional drug- and vaccine-development methods, there is a need for a new AI-based platform that can revolutionize the industry. One approach involves utilizing smartphones equipped with medical sensors to collect and transmit real-time physiological and healthcare information on clinical-trial participants to the nearest edge nodes (EN). This allows the verification of a vast amount of medical data for a large number of individuals in a short time frame, without the restrictions of latency, bandwidth, or security constraints. The collected information can be monitored by physicians and researchers to assess a vaccine’s performance.

Background Helicobacter pylori ( H. pylori ) is a bacterial infection that is prevalent and affects more than half of the world's population, causing stomach disorders such as gastritis , peptic ulcer disease, and gastric cancer. Main body The diagnosis of H. pylori infection relies on invasive and non-invasive techniques emerging artificial intelligence, and antibiotic therapy is available, but antibiotic resistance is a growing concern. The development of a vaccine is crucial in preventing H. pylori -associated diseases, but it faces challenges due to the bacterium's variability and immune escape mechanisms. Despite the challenges, ongoing research into H. pylori's virulence factors and immune escape mechanisms, as well as the development of potential vaccine targets, provides hope for more effective management and prevention of H. pylori -associated diseases. Recent research on H. pylori's immune escape mechanisms and novel immune checkpoint inhibitors could also lead to biomarkers for early cancer detection. Therefore, experts have suggested a combination of traditional and herbal medicine with artificial intelligence to potentially eradicate H. pylori . Short conclusion H. pylori infection remains a significant global health problem, but ongoing research into its properties and advanced technologies in addition to the combination of traditional and herbal medicine with artificial intelligence may also lead to the eradication of H. pylori -associated diseases. Graphical abstract