Using machine learning techniques to predict antimicrobial resistance in stone disease patients.

PURPOSE: Artificial intelligence is part of our daily life and machine learning techniques offer possibilities unknown until now in medicine. This study aims to offer an evaluation of the performance of machine learning (ML) techniques, for predicting bacterial resistance in a urology department. METHODS: Data were retrieved from laboratory information system (LIS) concerning 239 patients with urolithiasis hospitalized in the urology department of a tertiary hospital over a 1-year period (2019): age, gender, Gram stain (positive, negative), bacterial species, sample type, antibiotics and antimicrobial susceptibility. In our experiments, we compared several classifiers following a tenfold cross-validation approach on 2 different versions of our dataset; the first contained only information of Gram stain, while the second had knowledge of bacterial species. RESULTS: The best results in the balanced dataset containing Gram stain, achieve a weighted average receiver operator curve (ROC) area of 0.768 and F-measure of 0.708, using a multinomial logistic regression model with a ridge estimator. The corresponding results of the balanced dataset, that contained bacterial species, achieve a weighted average ROC area of 0.874 and F-measure of 0.783, with a bagging classifier. CONCLUSIONS: Artificial intelligence technology can be used for making predictions on antibiotic resistance patterns when knowing Gram staining with an accuracy of 77% and nearly 87% when identifying specific microorganisms. This knowledge can aid urologists prescribing the appropriate antibiotic 24-48 h before test results are known.

Fragility index of urological literature regarding medical expulsive treatment.

INTRODUCTION: The role of medical expulsive treatment (MET) is controversial. Fragility index is an additional metric to assess randomized controlled trials (RCTs) outcome validity and indicates how many patients would be required to convert a trial from being statistically significant, to not significant. The larger is the FI, the better the trial's data. The aim of this study is to assess FI of RCTs regarding MET for ureteral stones. MATERIALS AND METHODS: A systematic literature search was performed. RCTs, reporting stone expulsion as a dichotomous outcome, showing statistical significance were eligible. FI (the number of patients needed to change from a non-event to event group, to lose statistical significance) and Fragility quotient (FI divided by total sample size), were calculated while Pearson's correlation and Mann-Whitney U test were used as appropriate. RESULTS: Thirty-six RCTs were eligible, with median FI = 3.5 and fragility quotient = 0.042, median sample size = 81, median journal impact factor = 1.73 and median reported p value = 0.008. In 33.3% of the studies, number of patients lost during follow-up was larger than FI, while in 13.89% of the studies, FI was 0, indicating use of inappropriate statistical method. Pearson's correlation showed significant positive association between FI and sample size (r = 0.981), number of events (r = 0.982) and impact factor (r = 0.731), while no association was found with p value or publication year. CONCLUSIONS: In this analysis, a calculated FI of 3.5 indicates that findings from RCTs on MET for ureteral stones are fragile and should be interpreted in combination with clinical thinking and expertise.

On Using Linear Diophantine Equations for in-Parallel Hiding of Decision Tree Rules.

Data sharing among organizations has become an increasingly common procedure in several areas such as advertising, marketing, electronic commerce, banking, and insurance sectors. However, any organization will most likely try to keep some patterns as hidden as possible once it shares its datasets with others. This paper focuses on preserving the privacy of sensitive patterns when inducing decision trees. We adopt a record augmentation approach to hide critical classification rules in binary datasets. Such a hiding methodology is preferred over other heuristic solutions like output perturbation or cryptographic techniques, which limit the usability of the data, since the raw data itself is readily available for public use. We propose a look ahead technique using linear Diophantine equations to add the appropriate number of instances while maintaining the initial entropy of the nodes. This method can be used to hide one or more decision tree rules optimally.

Using Minimum Local Distortion to Hide Decision Tree Rules.

The sharing of data among organizations has become an increasingly common procedure in several areas like banking, electronic commerce, advertising, marketing, health, and insurance sectors. However, any organization will most likely try to keep some patterns hidden once it shares its datasets with others. This article focuses on preserving the privacy of sensitive patterns when inducing decision trees. We propose a heuristic approach that can be used to hide a certain rule which can be inferred from the derivation of a binary decision tree. This hiding method is preferred over other heuristic solutions like output perturbation or cryptographic techniques-which limit the usability of the data-since the raw data itself is readily available for public use. This method can be used to hide decision tree rules with a minimum impact on all other rules derived.

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.

Corneal changes after large (9mm) lateral rectus muscle recession measured with Pentacam®.

Introduction: The aim of this study was to evaluate alterations in corneal astigmatism, axial anterior corneal curvature, anterior chamber depth, and central corneal thickness (CCT) two months after the unilateral recession of lateral rectus muscle in children. Methods: This prospective study included 37 children with intermittent exotropia who would undergo unilateral lateral rectus muscle recession. All measurements were performed using Pentacam®. Comparisons were made between the operated and fellow unoperated eyes, pre- and post-operatively. The assessment was made for changes in the radius of axial curvature on major meridians at 3 and 3.5 mm from the optical corneal center in the mid-peripheral zone. Astigmatism changes of the anterior and posterior corneal surface were calculated using vector analysis software (astigMATIC®). The interaction between age or CCT and postoperative changes in anterior and posterior surface corneal astigmatism were examined with ANOVA model. Results: In the intervention group, changes in anterior and posterior corneal surface astigmatism were statistically significant, with a mean increase of 0.56Dx90 and 0.08Dx87, respectively. In the mid-peripheral corneal zone, an increase was observed in the radius of anterior corneal axial curvature, more evident temporal 3 and 3.5 mm from the corneal center on the horizontal meridian, with corresponding decrease superiorly and inferiorly at 3 and 3.5 mm from the corneal center on the vertical meridian. Discussion: The changes in total astigmatism of the operated eyes are mainly attributed to the anterior corneal surface. These changes are associated with flattening in the 180 meridian of the cornea, leading to a shift to "with-the-rule" astigmatism.

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.

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.

Nationwide mortality trends from 2001 to 2020 in Greece: health policy implications under the scope of aging societies.

OBJECTIVE: This nationwide study aims to analyze mortality trends for all individual causes in Greece from 2001 to 2020, with a specific focus on 2020, a year influenced by the COVID-19 pandemic. As Greece is the fastest-aging country in Europe, the study's findings can be generalized to other aging societies, guiding the reevaluation of global health policies. METHODS: Data on the population and the number of deaths were retrieved from the Hellenic Statistical Authority. We calculated age-standardized mortality rates (ASMR) and cause-specific mortality rates by sex in three age groups (0-64, 65-79, and 80+ years) from 2001 to 2020. Proportional mortality rates for 2020 were determined. Statistical analysis used generalized linear models with Python Programming Language. RESULTS: From 2001 to 2020, the ASMR of cardiovascular diseases (CVD) decreased by 42.7% (p < 0.0001), with declines in most sub-causes, except for hypertensive diseases, which increased by 2.8-fold (p < 0.0001). In 2020, the proportional mortality rates of the three leading causes were 34.9% for CVD, 23.5% for neoplasms, and 9.6% for respiratory diseases (RD). In 2020, CVD were the leading cause of death among individuals aged 80+ years (39.3%), while neoplasms were the leading cause among those aged 0-79 years (37.7%). Among cardiovascular sub-causes, cerebrovascular diseases were predominant in the 80+ year age group (30.3%), while ischemic heart diseases were most prevalent among those aged 0-79 years (up to 60.0%). CONCLUSIONS: The global phenomenon of population aging necessitates a reframing of health policies in our aging societies, focusing on diseases with either a high mortality burden, such as CVD, neoplasms, and RD, or those experiencing increasing trends, such as hypertensive diseases.

Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review.

With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery". Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ''master-slave'' robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.

Corneal tomography changes following major (6 mm) medial rectus muscle recession: a prospective cohort study using Pentacam®.

INTRODUCTION: The aim of this study is to evaluate changes in corneal astigmatism, axial anterior corneal curvature, as well as changes in the anterior chamber depth and central corneal thickness, 2 months following the unilateral recession of medial rectus muscle in children. METHODS: Thirty-three children with esotropia were prospectively evaluated following unilateral medial rectus muscle recession, using Pentacam®. Comparisons were made between the operated and fellow unoperated eyes, pre, and postoperatively. The assessment was made for changes in the radius of axial curvature on major meridians at 3 and 3.5 mm from the optical corneal center in the mid-peripheral zone. Astigmatism changes of the anterior and posterior corneal surface were calculated using vector analysis software (astigMATIC®). ANOVA model was used to examine the interaction between age or central corneal thickness and postoperative changes in anterior and posterior surface corneal astigmatism. RESULTS: In the intervention group, changes in anterior and posterior corneal surface astigmatism were statistically significant, with a mean increase of 0.59Dx92 and 0.08Dx91, respectively. In the mid-peripheral corneal zone, there is an increase in the radius of anterior corneal axial curvature more evident nasally 3.5 mm from the corneal center on the horizontal meridian, with corresponding decrease superiorly and inferiorly at 3 and 3.5 mm from the corneal center on the vertical meridian. DISCUSSION: The changes in total astigmatism of the operated eyes are mainly attributed to the anterior corneal surface. These changes are associated with flattening in the 180 meridian of the cornea, leading to a shift to "with-the-rule" astigmatism.

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.

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.

ChatGPT in Clinical Medicine, Urology and Academia: A Review.

BACKGROUND: This study aims to provide a comprehensive overview of the current literature on the utilisation of ChatGPT in the fields of clinical medicine, urology, and academic medicine, while also addressing the associated ethical challenges and potential risks. METHODS: This narrative review conducted an extensive search of the PubMed and MEDLINE databases, covering the period from January 2022 to January 2024. The search phrases employed were "urologic surgery" in conjunction with "artificial intelligence", "machine learning", "neural network", "ChatGPT", "urology", and "medicine". The initial studies were chosen from the screened research to examine the possible interaction between those entities. Research utilising animal models was excluded. RESULTS: ChatGPT has demonstrated its usefulness in clinical settings by producing precise clinical correspondence, discharge summaries, and medical records, thereby assisting in these laborious tasks, especially with the latest iterations of ChatGPT. Furthermore, patients can access essential medical information by inquiring with ChatGPT. Nevertheless, there are multiple concerns regarding the correctness of the system, including allegations of falsified data and references. These issues emphasise the importance of having a doctor oversee the final result to guarantee patient safety. ChatGPT shows potential in academic medicine for generating drafts and organising datasets. However, the presence of guidelines and plagiarism-detection technologies is necessary to mitigate the risks of plagiarism and the use of faked data when using it for academic purposes. CONCLUSIONS: ChatGPT should be utilised as a supplementary tool by urologists and academicians. However, it is now advisable to have human oversight to guarantee patient safety, uphold academic integrity, and maintain transparency.

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.

From Affect Regulation to BMI: Unveiling Childhood Obesity Patterns Through Advanced Clustering Techniques.

This study explores the relationship between psychological factors and children's BMI, using clustering methods like Gaussian Mixture Models and Spectral Clustering. Affinity Propagation was particularly effective, suggesting that tailored interventions based on psychological assessments could improve obesity management in children.

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.

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.

Prediction of COVID-19 Mortality in the Intensive Care Unit Using Machine Learning.

Since its emergence, the COVID-19 pandemic still poses a major global health threat. In this setting, a number of useful machine learning applications have been explored to assist clinical decision-making, predict the severity of disease and admission to the intensive care unit, and also to estimate future demand for hospital beds, equipment, and staff. The present study examined demographic data, hematological and biochemical markers routinely measured in Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, in relation to the ICU outcome, during the second and third Covid-19 waves, from October 2020 until February 2022. In this dataset, we applied eight well-known classifiers of the caret package for machine learning of the R programming language, to evaluate their performance in forecasting ICU mortality. The best performance regarding area under the receiver operating characteristic curve (AUC-ROC) was observed with Random Forest (0.82), while k-nearest neighbors (k-NN) were the lowest performing machine learning algorithm (AUC-ROC: 0.59). However, in terms of sensitivity, XGB outperformed the other classifiers (max Sens: 0.7). The six most important predictors of mortality in the Random Forest model were serum urea, age, hemoglobin, C-reactive protein, platelets, and lymphocyte count.

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.