Enhancing Pulmonary Embolism Detection in COVID-19 Patients Through Advanced Deep Learning Techniques.

The intersection of COVID-19 and pulmonary embolism (PE) has posed unprecedented challenges in medical diagnostics. The critical nature of PE and its increased incidence during the pandemic underline the need for improved detection methods. This study evaluates the effectiveness of advanced deep learning techniques in enhancing PE detection in post-COVID-19 patients through Computed Tomography Pulmonary Angiography (CTPA) scans. Using a dataset of 746 anonymized CTPA images from 25 patients, we fine-tuned the state-of-the-art Ultralytics YOLOv8 object detection model, which was trained on 676 images with 1,517 annotated bounding boxes and validated on 70 images with 108 bounding boxes. After 200 epochs of training, which lasted approximately 1.021 hours, the YOLOv8 model demonstrated significant diagnostic proficiency, achieving a mean Average Precision (mAP) of 0.683 at an IoU threshold of 0.50 and a mAP of 0.246 at the IoU range of 0.50:0.95 in the validation dataset. Notably, the model reached a maximum precision of 0.85949 and a maximum recall of 0.81481, though these metrics were observed in separate epochs. These findings emphasize the model's potential for high diagnostic accuracy and offer a promising direction for deploying AI tools in clinical settings, significantly contributing to healthcare innovation and patient care post-pandemic.

Evaluating ChatGPT 4.0's User Satisfaction Among Doctors Across Different Medical Departments.

In an era increasingly focused on integrating Artificial Intelligence (AI) into healthcare, the utility and user satisfaction of AI applications like ChatGPT have become pivotal research areas. This study, conducted in Greece, engaged 193 doctors from various medical departments who interacted with ChatGPT 4.0 through a custom web application. The participants, representing a diverse range of medical specialties, received responses from the specific chatbot tailored to their specific departmental inquiries. Their satisfaction was gauged using a validated form featuring a 1-to-5 rating scale. The results highlighted a possible correlation between the doctors' medical departments and their satisfaction levels with ChatGPT 4.0. Significantly, doctors from certain departments (like General Surgery and Cardiology) reported lower satisfaction scores, ranging from 2.73 to 2.80 out of 5, in contrast to their colleagues from departments like Biopathology and Orthopedics, who scored between 4.00 and 4.46 out of 5. This variation in satisfaction levels underscores the diverse needs within different medical specialties and illuminates both the potential of ChatGPT and the areas needing improvement, especially in delivering department-specific medical information. Despite its limitations, ChatGPT version 4.0 is emerging as a valuable tool in the medical community, indicating potential future advancements and more extensive integration into healthcare practices. The study's findings are crucial in understanding the distinct preferences and requirements of healthcare professionals across various medical departments, thereby guiding the future development of AI tools in healthcare.

Comparative Analysis of Macular and Optic Disc Perfusion Pre and Post Silicone Oil Removal: A Machine Learning Approach.

In the realm of ophthalmic surgeries, silicone oil is often utilized as a tamponade agent for repairing retinal detachments, but it necessitates subsequent removal. This study harnesses the power of machine learning to analyze the macular and optic disc perfusion changes pre and post-silicone oil removal, using Optical Coherence Tomography Angiography (OCTA) data. Building upon the foundational work of prior research, our investigation employs Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) networks to create predictive models based on OCTA scans. We conducted a comparative analysis focusing on the flow in the outer retina and vessel density in the deep capillary plexus (superior-hemi and perifovea) to track perfusion changes across different time points. Our findings indicate that while machine learning models predict the flow in the outer retina with reasonable accuracy, predicting the vessel density in the deep capillary plexus (particularly in the superior-hemi and perifovea regions) remains challenging. These results underscore the potential of machine learning to contribute to personalized patient care in ophthalmology, despite the inherent complexities in predicting ocular perfusion changes.

Advancing Cardiovascular Mortality Trend Analysis: A Machine Learning Approach to Predict Future Health Policy Needs.

This study investigates the forecasting of cardiovascular mortality trends in Greece's elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook's Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.

Testosterone therapy for functional hypogonadism in middle-aged and elderly males: current evidence and future perspectives.

Population aging is a global phenomenon driving research focus toward preventing and managing age-related disorders. Functional hypogonadism (FH) has been defined as the combination of low testosterone levels, typically serum total testosterone below 300-350 ng/dL, together with manifestations of hypogonadism, in the absence of an intrinsic pathology of the hypothalamic-pituitary-testicular (HPT) axis. It is usually seen in middle-aged or elderly males as a product of aging and multimorbidity. This age-related decline in testosterone levels has been associated with numerous adverse outcomes. Testosterone therapy (TTh) is the mainstay of treatment for organic hypogonadism with an identifiable intrinsic pathology of the HPT axis. Current guidelines generally make weak recommendations for TTh in patients with FH, mostly in the presence of sexual dysfunction. Concerns about long-term safety have historically limited TTh use in middle-aged and elderly males with FH. However, recent randomized controlled trials and meta-analyses have demonstrated safe long-term outcomes regarding prostatic and cardiovascular health, together with decreases in all-cause mortality and improvements in various domains, including sexual function, body composition, physical strength, bone density, and hematopoiesis. Furthermore, there are numerous insightful studies suggesting additional benefits of TTh, for instance in cardio-renal-metabolic conditions. Specifically, future trials should investigate the role of TTh in improving symptoms and prognosis in various clinical contexts, including sarcopenia, frailty, dyslipidemia, arterial hypertension, diabetes mellitus, fracture risk, heart failure, stable angina, chronic kidney disease, mood disorders, and cognitive dysfunction.

The Contemporary Role of Salvage Radical Prostatectomy in the Management of Recurrent Prostate Cancer: An Up-to-Date Review.

Prostate cancer is the second most common cancer among men, with many treatment modalities available for patients, such as radical prostatectomy, external beam radiotherapy, brachytherapy, high-intensity focused ultrasound, cryotherapy, electroporation and other whole-gland or focal ablative novel techniques. Unfortunately, up to 60% of men with prostate cancer experience recurrence at 5 to 10 years. Salvage radical prostatectomy can be offered as an option in the setting of recurrence after a primary non-surgical treatment. However, the complexity of salvage radical prostatectomy is considered to be greater than that of primary surgery, making it the least popular treatment of choice. With the wide use of robotic platforms in urologic oncologic surgery, salvage radical prostatectomy has attracted attention again because, compared to past data, modern series involving salvage Robot-Assisted Radical Prostatectomy have shown promising results. In this narrative literature review, we comprehensively examined data on salvage radical prostatectomy. We investigated the correlation between the different types of primary prostate cancer therapy and the following salvage radical prostatectomy. Furthermore, we explored the concept of a robotic approach and its beneficial effect in salvage surgery. Lastly, we emphasized several promising avenues for future research in this field.

Macular and Optic Disc Perfusion Changes After Silicone Oil Removal Using Optical Coherence Tomography Angiography: A Prospective Study.

AIM: The aim of this study was to prospectively evaluate the changes in macular and optic disc microvascular structures in patients who underwent silicone oil (SO) removal. MATERIALS AND METHODS: A total of 28 patients scheduled for unilateral SO removal were included in the study. Their fellow eyes served as controls. Optical coherence tomography angiography (OCTA) of the retina (6.0 mm) and disc (4.5 mm) was performed one day before SO removal, and then at 1 week and 1, 3, 6, and 12 months postoperatively. All analyses were conducted using the R programming language, with a p-value <0.05 considered statistically significant. RESULTS: After silicone oil removal, statistically significant changes were observed in the flow in the outer retina and radial peripapillary capillary (RPC) density for small and all vessels inside the disc. Statistically significant differences between the intervention and control groups were noted in vessel density in both the superficial and deep capillary plexuses and RPC density for small and all vessels. CONCLUSION: Changes in macular vessel density and radial peripapillary capillary density were observed after SO removal. The latter changes appear to improve after the first postoperative month and continue until the first postoperative year. Notably, these changes were significant between the first postoperative week and 6 and 12 postoperative months (p = 0.0263 and p = 0.021, respectively). Best corrected visual acuity (BCVA) is likely associated with these parameters, indicating that improvement may be observed even one year following SO removal.

Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review.

This comprehensive review critically examines the transformative impact of artificial intelligence (AI) and radiomics in the diagnosis, prognosis, and management of bladder, kidney, and prostate cancers. These cutting-edge technologies are revolutionizing the landscape of cancer care, enhancing both precision and personalization in medical treatments. Our review provides an in-depth analysis of the latest advancements in AI and radiomics, with a specific focus on their roles in urological oncology. We discuss how AI and radiomics have notably improved the accuracy of diagnosis and staging in bladder cancer, especially through advanced imaging techniques like multiparametric MRI (mpMRI) and CT scans. These tools are pivotal in assessing muscle invasiveness and pathological grades, critical elements in formulating treatment plans. In the realm of kidney cancer, AI and radiomics aid in distinguishing between renal cell carcinoma (RCC) subtypes and grades. The integration of radiogenomics offers a comprehensive view of disease biology, leading to tailored therapeutic approaches. Prostate cancer diagnosis and management have also seen substantial benefits from these technologies. AI-enhanced MRI has significantly improved tumor detection and localization, thereby aiding in more effective treatment planning. The review also addresses the challenges in integrating AI and radiomics into clinical practice, such as the need for standardization, ensuring data quality, and overcoming the "black box" nature of AI. We emphasize the importance of multicentric collaborations and extensive studies to enhance the applicability and generalizability of these technologies in diverse clinical settings. In conclusion, AI and radiomics represent a major paradigm shift in oncology, offering more precise, personalized, and patient-centric approaches to cancer care. While their potential to improve diagnostic accuracy, patient outcomes, and our understanding of cancer biology is profound, challenges in clinical integration and application persist. We advocate for continued research and development in AI and radiomics, underscoring the need to address existing limitations to fully leverage their capabilities in the field of oncology.

Automated ML Techniques for Predicting COVID-19 Mortality in the ICU.

The COVID-19 infection is still a serious threat to public health and healthcare systems. Numerous practical machine learning applications have been investigated in this context to support clinical decision-making, forecast disease severity and admission to the intensive care unit, as well as to predict the demand for hospital beds, equipment, and staff in the future. We retrospectively analyzed demographics, and routine blood biomarkers from consecutive Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, during a 17-month period, relative to the outcome, in order to build a prognostic model. We used the Google Vertex AI platform, on the one hand, to evaluate its performance in predicting ICU mortality, and on the other hand to show the ease with which even non-experts can make prognostic models. The model's performance regarding the area under the receiver operating characteristic curve (AUC-ROC) was 0.955. The six highest-ranked predictors of mortality in the prognostic model were age, serum urea, platelets, C-reactive protein, hemoglobin, and SGOT.

A Deep Learning Model for Classifying Histological Types of Colorectal Polyps.

In this study a deep learning architecture based on a convolutional neural network has been evaluated for the classification of white light images of colorectal polyps acquired during the process of a colonoscopy, to estimate the accuracy of the optical recognition of histologic types of polyps. Convolutional neural networks (CNNs), a subclass of artificial neural networks that have gained dominance in several computer vision tasks, are gaining popularity in many medical fields, including endoscopy. The TensorFlow framework was used for implementing EfficientNetB7, which was trained with 924 images, drawn from 86 patients. 55% of the polyps were adenomas, 22% were hyperplastic, and 17% were lesions with sessile serrations. The validation loss, accuracy, and AUC ROC were 0.4845, 0.7778, and 0.8881 respectively.

Acceptance of Artificial Intelligence in Supporting Cancer Patients.

ASCAPE Project is a study aiming to implement the advances of Artificial Intelligence (AI), to support prostate cancer survivors, regarding quality of life issues. The aim of the study is to determine characteristics of patients who accepted to join ASCAPE project. It results that participants of the study mainly originate from higher-educated societies that are better informed about the potential benefits of AI in medicine. Therefore, efforts should be focused on eliminating patients' reluctancy by better informing them on the potential benefits of AI.

Training ChatGPT Models in Assisting Urologists in Daily Practice.

Artificial Intelligence (AI) has shown the ability to enhance the accuracy and efficiency of physicians. ChatGPT is an AI chatbot that can interact with humans through text, over the internet. It is trained with machine learning algorithms, using large datasets. In this study, we compare the performance of using a ChatGPT API 3.5 Turbo model to a general model, in assisting urologists in obtaining accurate, valid medical information. The API was accessed through a Python script that was applied specifically for this study based on 2023 EAU guidelines in PDF format. This custom-trained model leads to providing doctors with more precise, prompt answers about specific urologic subjects, thus helping them, ultimately, providing better patient care.

Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review.

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.

Using Association Rules in Antimicrobial Resistance in Stone Disease Patients.

Association rule mining is a very popular unsupervised machine learning technique for discovering patterns in large datasets. Patients with stone disease commonly suffer from urinary tract infections (UTI), complicated by the emergence of antimicrobial resistance (AMR), due to the excessive use of antibiotics. In this study, we explore the use of association rule mining in the AMR profile of patients suffering from stone disease.

Cluster Analysis Assessment in Proposing a Surgical Technique for Benign Prostatic Enlargement.

Benign prostatic enlargement (BPE) is a common disease in men over 50 years old. The phenotype of patients with BPE is heterogenous, regarding both baseline patient characteristics and disease-related parameters. Treatment can be either medical-conservative or surgical. A great variety of surgical techniques are available for surgical management, with three of the most common being monopolar transurethral resection of the prostate (mTUR-P), bipolar transurethral resection of the prostate (bTUR-P), and bipolar transurethral vaporization of the prostate (bTUVis). The selection of each one of these depends on surgeon reasoning, equipment availability, patient characteristics, and preferences. Since all of these techniques are available in our Urology Department, and surgeons are skilled to perform each one of them, we performed a clustering analysis according to patient pre-operative characteristics, using the k-means algorithm, to compare clustering-related technique assignment with the real-life technique used.

Inference Control in a Diabetes Data Set Using a Java-Based Prototype of LDH Algorithm.

Data sharing among different entities in the healthcare domain has become an increasingly common practice, where each entity would most likely want to prevent indirect data disclosure via inference channels. The Local Distortion Hiding (LDH) algorithm has been developed to protect sensitive decision tree (DT) rules, which are chosen not to be disclosed when DT construction techniques are applied to the data. This article presents eight experiments using a Java-based prototype that implements the LDH algorithm in a diabetes data set. Our experiments test the ability of the LDH algorithm in two ways, firstly in inference control and secondly in maintaining the structure and the performance metrics of the resulting DT. Our experiments on hiding eight terminal nodes in a diabetes data set using a Java-based prototype that implements the LDH algorithm, yield satisfactory results.

Revealing latent traits in the social behavior of distance learning students.

This paper proposes a multilayered methodology for analyzing distance learning students' data to gain insight into the learning progress of the student subjects both in an individual basis and as members of a learning community during the course taking process. The communication aspect is of high importance in educational research. Additionally, it is difficult to assess as it involves multiple relationships and different levels of interaction. Social network analysis (SNA) allows the visualization of this complexity and provides quantified measures for evaluation. Thus, initially, SNA techniques were applied to create one-mode, undirected networks and capture important metrics originating from students' interactions in the fora of the courses offered in the context of distance learning programs. Principal component analysis and clustering were used next to reveal latent students' traits and common patterns in their social interactions with other students and their learning behavior. We selected two different courses to test this methodology and to highlight convergent and divergent features between them. Three major factors that explain over 70% of the variance were identified and four groups of students were found, characterized by common elements in students' learning profile. The results highlight the importance of academic performance, social behavior and online participation as the main criteria for clustering that could be helpful for tutors in distance learning to closely monitor the learning process and promptly interevent when needed.

Analyzing Sequence Data with Markov Chain Models in Scientific Experiments.

Virtual reality-based instruction is becoming an important resource to improve learning outcomes and communicate hands-on skills in science laboratory courses. Our study attempts first to investigate whether a Markov chain model can predict the students' performance in conducting an experiment and whether simulations improve learner achievement in handling lab equipment and conducting science experiments in physical labs. In the present study, three cohorts of graduate students are trained on a microscopy experiment using different teaching methodologies. The effectiveness of the teaching strategies is evaluated by observing the sequences of students' actions, while engaging in the microscopy experiment in real-lab situations. The students' ability in performing the science experiment is estimated by sequential analysis using a Markov chain model. According to the Markov chain analysis, the students who are trained via a virtual reality software exhibit a higher probability to perform the steps of the experiment without difficulty and without assistance than their fellow students who attend more traditional training scenarios. Our study indicates that a Markov chain model is a powerful tool that can lead to a dynamic evaluation of the students' performance in science experiments by tracing the students' knowledge states and by predicting their innate abilities.

Local Distortion Hiding Algorithm in Medical Data: A Case Study Using CART.

Data sharing has become an increasingly common process among health organizations, but any organization will most likely try to hide some sensitive patterns before sharing its data with others. Local Distortion Hiding (LDH), a recently proposed algorithm, has been evaluated only on the assumption of an opponent using the J48 (C4.5) classification algorithm. We now extend the basic approach, and we present a medical dataset hiding case study of a processed by LDH and attacked with the CART algorithm.

Hiding Decision Tree Rules in Medical Data: A Case Study.

Data sharing among health organizations has become an increasingly common process, but any organization will most likely try to hide some sensitive patterns before it shares its data with others. This article focuses on the protection of sensitive patterns when we assume that decision trees will be the models to be induced. We apply a heuristic approach to hideany arbitrary rule from the derivation of a binary decision tree. The proposed hiding method is preferred over other heuristic solutions such as output disturbance or encryption methods that limit data usability, as the raw data itself can then more easily be offered for access by any third parties.