Heparin treatment is associated with a delayed diagnosis of Alzheimer's dementia in electronic health records from two large United States health systems.

Recent studies suggest that heparan sulfate proteoglycans (HSPG) contribute to the predisposition to, protection from, and potential treatment and prevention of Alzheimer's disease (AD). Here, we used electronic health records (EHR) from two different health systems to examine whether heparin therapy was associated with a delayed diagnosis of AD dementia. Longitudinal EHR data from 15,183 patients from the Mount Sinai Health System (MSHS) and 6207 patients from Columbia University Medical Center (CUMC) were used in separate survival analyses to compare those who did or did not receive heparin therapy, had a least 5 years of observation, were at least 65 years old by their last visit, and had subsequent diagnostic code or drug treatment evidence of possible AD dementia. Analyses controlled for age, sex, comorbidities, follow-up duration and number of inpatient visits. Heparin therapy was associated with significant delays in age of clinical diagnosis of AD dementia, including +1.0 years in the MSMS cohort (P < 0.001) and +1.0 years in the CUMC cohort (P < 0.001). While additional studies are needed, this study supports the potential roles of heparin-like drugs and HSPGs in the protection from and prevention of AD dementia.

Exploring the role of Large Language Models in haematology: A focused review of applications, benefits and limitations.

Large language models (LLMs) have significantly impacted various fields with their ability to understand and generate human-like text. This study explores the potential benefits and limitations of integrating LLMs, such as ChatGPT, into haematology practices. Utilizing systematic review methodologies, we analysed studies published after 1 December 2022, from databases like PubMed, Web of Science and Scopus, and assessing each for bias with the QUADAS-2 tool. We reviewed 10 studies that applied LLMs in various haematology contexts. These models demonstrated proficiency in specific tasks, such as achieving 76% diagnostic accuracy for haemoglobinopathies. However, the research highlighted inconsistencies in performance and reference accuracy, indicating variability in reliability across different uses. Additionally, the limited scope of these studies and constraints on datasets could potentially limit the generalizability of our findings. The findings suggest that, while LLMs provide notable advantages in enhancing diagnostic processes and educational resources within haematology, their integration into clinical practice requires careful consideration. Before implementing them in haematology, rigorous testing and specific adaptation are essential. This involves validating their accuracy and reliability across different scenarios. Given the field's complexity, it is also critical to continuously monitor these models and adapt them responsively.

Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis.

OBJECTIVES: Scaphoid fractures are usually diagnosed using X-rays, a low-sensitivity modality. Artificial intelligence (AI) using Convolutional Neural Networks (CNNs) has been explored for diagnosing scaphoid fractures in X-rays. The aim of this systematic review and meta-analysis is to evaluate the use of AI for detecting scaphoid fractures on X-rays and analyze its accuracy and usefulness. MATERIALS AND METHODS: This study followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy. A literature search was conducted in the PubMed database for original articles published until July 2023. The risk of bias and applicability were evaluated using the QUADAS-2 tool. A bivariate diagnostic random-effects meta-analysis was conducted, and the results were analyzed using the Summary Receiver Operating Characteristic (SROC) curve. RESULTS: Ten studies met the inclusion criteria and were all retrospective. The AI's diagnostic performance for detecting scaphoid fractures ranged from AUC 0.77 to 0.96. Seven studies were included in the meta-analysis, with a total of 3373 images. The meta-analysis pooled sensitivity and specificity were 0.80 and 0.89, respectively. The meta-analysis overall AUC was 0.88. The QUADAS-2 tool found high risk of bias and concerns about applicability in 9 out of 10 studies. CONCLUSIONS: The current results of AI's diagnostic performance for detecting scaphoid fractures in X-rays show promise. The results show high overall sensitivity and specificity and a high SROC result. Further research is needed to compare AI's diagnostic performance to human diagnostic performance in a clinical setting. CLINICAL RELEVANCE STATEMENT: Scaphoid fractures are prone to be missed secondary to assessment with a low sensitivity modality and a high occult fracture rate. AI systems can be beneficial for clinicians and radiologists to facilitate early diagnosis, and avoid missed injuries. KEY POINTS: • Scaphoid fractures are common and some can be easily missed in X-rays. • Artificial intelligence (AI) systems demonstrate high diagnostic performance for the diagnosis of scaphoid fractures in X-rays. • AI systems can be beneficial in diagnosing both obvious and occult scaphoid fractures.

Assessing GPT-4 multimodal performance in radiological image analysis.

OBJECTIVES: This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical regions, and pathologies to explore the potential of zero-shot generative AI in enhancing diagnostic processes in radiology. METHODS: We analyzed 230 anonymized emergency room diagnostic images, consecutively collected over 1 week, using GPT-4V. Modalities included ultrasound (US), computerized tomography (CT), and X-ray images. The interpretations provided by GPT-4V were then compared with those of senior radiologists. This comparison aimed to evaluate the accuracy of GPT-4V in recognizing the imaging modality, anatomical region, and pathology present in the images. RESULTS: GPT-4V identified the imaging modality correctly in 100% of cases (221/221), the anatomical region in 87.1% (189/217), and the pathology in 35.2% (76/216). However, the model's performance varied significantly across different modalities, with anatomical region identification accuracy ranging from 60.9% (39/64) in US images to 97% (98/101) and 100% (52/52) in CT and X-ray images (p < 0.001). Similarly, pathology identification ranged from 9.1% (6/66) in US images to 36.4% (36/99) in CT and 66.7% (34/51) in X-ray images (p < 0.001). These variations indicate inconsistencies in GPT-4V's ability to interpret radiological images accurately. CONCLUSION: While the integration of AI in radiology, exemplified by multimodal GPT-4, offers promising avenues for diagnostic enhancement, the current capabilities of GPT-4V are not yet reliable for interpreting radiological images. This study underscores the necessity for ongoing development to achieve dependable performance in radiology diagnostics. CLINICAL RELEVANCE STATEMENT: Although GPT-4V shows promise in radiological image interpretation, its high diagnostic hallucination rate (> 40%) indicates it cannot be trusted for clinical use as a standalone tool. Improvements are necessary to enhance its reliability and ensure patient safety. KEY POINTS: GPT-4V's capability in analyzing images offers new clinical possibilities in radiology. GPT-4V excels in identifying imaging modalities but demonstrates inconsistent anatomy and pathology detection. Ongoing AI advancements are necessary to enhance diagnostic reliability in radiological applications.

AI in the ED: Assessing the efficacy of GPT models vs. physicians in medical score calculation.

BACKGROUND AND AIMS: Artificial Intelligence (AI) models like GPT-3.5 and GPT-4 have shown promise across various domains but remain underexplored in healthcare. Emergency Departments (ED) rely on established scoring systems, such as NIHSS and HEART score, to guide clinical decision-making. This study aims to evaluate the proficiency of GPT-3.5 and GPT-4 against experienced ED physicians in calculating five commonly used medical scores. METHODS: This retrospective study analyzed data from 150 patients who visited the ED over one week. Both AI models and two human physicians were tasked with calculating scores for NIH Stroke Scale, Canadian Syncope Risk Score, Alvarado Score for Acute Appendicitis, Canadian CT Head Rule, and HEART Score. Cohen's Kappa statistic and AUC values were used to assess inter-rater agreement and predictive performance, respectively. RESULTS: The highest level of agreement was observed between the human physicians (Kappa = 0.681), while GPT-4 also showed moderate to substantial agreement with them (Kappa values of 0.473 and 0.576). GPT-3.5 had the lowest agreement with human scorers. These results highlight the superior predictive performance of human expertise over the currently available automated systems for this specific medical outcome. Human physicians achieved a higher ROC-AUC on 3 of the 5 scores, but none of the differences were statistically significant. CONCLUSIONS: While AI models demonstrated some level of concordance with human expertise, they fell short in emulating the complex clinical judgments that physicians make. The study suggests that current AI models may serve as supplementary tools but are not ready to replace human expertise in high-stakes settings like the ED. Further research is needed to explore the capabilities and limitations of AI in emergency medicine.

Glaucoma Prediction Models Based on Ocular and Systemic Findings.

INTRODUCTION: Our aim was to explore the impact of various systemic and ocular findings on predicting the development of glaucoma. METHODS: Medical records of 37,692 consecutive patients examined at a single medical center between 2001 and 2020 were analyzed using machine learning algorithms. Systemic and ocular features were included. Univariate and multivariate analyses followed by CatBoost and Light gradient-boosting machine prediction models were performed. Main outcome measures were systemic and ocular features associated with progression to glaucoma. RESULTS: A total of 7,880 patients (mean age 54.7 ± 12.6 years, 5,520 males [70.1%]) were included in a 3-year prediction model, and 314 patients (3.98%) had a final diagnosis of glaucoma. The combined model included 185 systemic and 42 ocular findings, and reached an ROC AUC of 0.84. The associated features were intraocular pressure (48.6%), cup-to-disk ratio (22.7%), age (8.6%), mean corpuscular volume (MCV) of red blood cell trend (5.2%), urinary system disease (3.3%), MCV (2.6%), creatinine level trend (2.1%), monocyte count trend (1.7%), ergometry metabolic equivalent task score (1.7%), dyslipidemia duration (1.6%), prostate-specific antigen level (1.2%), and musculoskeletal disease duration (0.5%). The ocular prediction model reached an ROC AUC of 0.86. Additional features included were age-related macular degeneration (10.0%), anterior capsular cataract (3.3%), visual acuity (2.0%), and peripapillary atrophy (1.3%). CONCLUSIONS: Ocular and combined systemic-ocular models can strongly predict the development of glaucoma in the forthcoming 3 years. Novel progression indicators may include anterior subcapsular cataracts, urinary disorders, and complete blood test results (mainly increased MCV and monocyte count).

Assessing Generative Pretrained Transformers (GPT) in Clinical Decision-Making: Comparative Analysis of GPT-3.5 and GPT-4.

BACKGROUND: Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement. OBJECTIVE: This study aims to analyze the performance of ChatGPT-3.5 and ChatGPT-4 in addressing complex clinical and ethical dilemmas, and to illustrate their potential role in health care decision-making while comparing seniors' and residents' ratings, and specific question types. METHODS: A total of 4 specialized physicians formulated 176 real-world clinical questions. A total of 8 senior physicians and residents assessed responses from GPT-3.5 and GPT-4 on a 1-5 scale across 5 categories: accuracy, relevance, clarity, utility, and comprehensiveness. Evaluations were conducted within internal medicine, emergency medicine, and ethics. Comparisons were made globally, between seniors and residents, and across classifications. RESULTS: Both GPT models received high mean scores (4.4, SD 0.8 for GPT-4 and 4.1, SD 1.0 for GPT-3.5). GPT-4 outperformed GPT-3.5 across all rating dimensions, with seniors consistently rating responses higher than residents for both models. Specifically, seniors rated GPT-4 as more beneficial and complete (mean 4.6 vs 4.0 and 4.6 vs 4.1, respectively; P<.001), and GPT-3.5 similarly (mean 4.1 vs 3.7 and 3.9 vs 3.5, respectively; P<.001). Ethical queries received the highest ratings for both models, with mean scores reflecting consistency across accuracy and completeness criteria. Distinctions among question types were significant, particularly for the GPT-4 mean scores in completeness across emergency, internal, and ethical questions (4.2, SD 1.0; 4.3, SD 0.8; and 4.5, SD 0.7, respectively; P<.001), and for GPT-3.5's accuracy, beneficial, and completeness dimensions. CONCLUSIONS: ChatGPT's potential to assist physicians with medical issues is promising, with prospects to enhance diagnostics, treatments, and ethics. While integration into clinical workflows may be valuable, it must complement, not replace, human expertise. Continued research is essential to ensure safe and effective implementation in clinical environments.

ChatGPT's adherence to otolaryngology clinical practice guidelines.

OBJECTIVES: Large language models, including ChatGPT, has the potential to transform the way we approach medical knowledge, yet accuracy in clinical topics is critical. Here we assessed ChatGPT's performance in adhering to the American Academy of Otolaryngology-Head and Neck Surgery guidelines. METHODS: We presented ChatGPT with 24 clinical otolaryngology questions based on the guidelines of the American Academy of Otolaryngology. This was done three times (N = 72) to test the model's consistency. Two otolaryngologists evaluated the responses for accuracy and relevance to the guidelines. Cohen's Kappa was used to measure evaluator agreement, and Cronbach's alpha assessed the consistency of ChatGPT's responses. RESULTS: The study revealed mixed results; 59.7% (43/72) of ChatGPT's responses were highly accurate, while only 2.8% (2/72) directly contradicted the guidelines. The model showed 100% accuracy in Head and Neck, but lower accuracy in Rhinology and Otology/Neurotology (66%), Laryngology (50%), and Pediatrics (8%). The model's responses were consistent in 17/24 (70.8%), with a Cronbach's alpha value of 0.87, indicating a reasonable consistency across tests. CONCLUSIONS: Using a guideline-based set of structured questions, ChatGPT demonstrates consistency but variable accuracy in otolaryngology. Its lower performance in some areas, especially Pediatrics, suggests that further rigorous evaluation is needed before considering real-world clinical use.

Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review.

OBJECTIVES: With smartphones and wearable devices becoming ubiquitous, they offer an opportunity for large-scale voice sampling. This systematic review explores the application of deep learning models for the automated analysis of voice samples to detect vocal cord pathologies. METHODS: We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. We searched MEDLINE and Embase databases for original publications on deep learning applications for diagnosing vocal cord pathologies between 2002 and 2022. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). RESULTS: Out of the 14 studies that met the inclusion criteria, data from a total of 3037 patients were analyzed. All studies were retrospective. Deep learning applications targeted Reinke's edema, nodules, polyps, cysts, unilateral cord paralysis, and vocal fold cancer detection. Most pathologies had detection accuracy above 90%. Thirteen studies (93%) exhibited a high risk of bias and concerns about applicability. CONCLUSIONS: Technology holds promise for enhancing the screening and diagnosis of vocal cord pathologies. While current research is limited, the presented studies offer proof of concept for developing larger-scale solutions.

Large language models for generating medical examinations: systematic review.

BACKGROUND: Writing multiple choice questions (MCQs) for the purpose of medical exams is challenging. It requires extensive medical knowledge, time and effort from medical educators. This systematic review focuses on the application of large language models (LLMs) in generating medical MCQs. METHODS: The authors searched for studies published up to November 2023. Search terms focused on LLMs generated MCQs for medical examinations. Non-English, out of year range and studies not focusing on AI generated multiple-choice questions were excluded. MEDLINE was used as a search database. Risk of bias was evaluated using a tailored QUADAS-2 tool. RESULTS: Overall, eight studies published between April 2023 and October 2023 were included. Six studies used Chat-GPT 3.5, while two employed GPT 4. Five studies showed that LLMs can produce competent questions valid for medical exams. Three studies used LLMs to write medical questions but did not evaluate the validity of the questions. One study conducted a comparative analysis of different models. One other study compared LLM-generated questions with those written by humans. All studies presented faulty questions that were deemed inappropriate for medical exams. Some questions required additional modifications in order to qualify. CONCLUSIONS: LLMs can be used to write MCQs for medical examinations. However, their limitations cannot be ignored. Further study in this field is essential and more conclusive evidence is needed. Until then, LLMs may serve as a supplementary tool for writing medical examinations. 2 studies were at high risk of bias. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Utilizing ChatGPT to Facilitate Referrals for Fetal Echocardiography.

INTRODUCTION: OpenAI's GPT-4 (artificial intelligence [AI]) is being studied for its use as a medical decision support tool. This research examines its accuracy in refining referrals for fetal echocardiography (FE) to improve early detection and outcomes related to congenital heart defects (CHDs). METHODS: Past FE data referred to our institution were evaluated separately by pediatric cardiologist, gynecologist (human experts [experts]), and AI, according to established guidelines. We compared experts and AI's agreement on referral necessity, with experts addressing discrepancies. RESULTS: Total of 59 FE cases were addressed retrospectively. Cardiologist, gynecologist, and AI recommended performing FE in 47.5%, 49.2%, and 59.0% of cases, respectively. Comparing AI recommendations to experts indicated agreement of around 80.0% with both experts (p < 0.001). Notably, AI suggested more echocardiographies for minor CHD (64.7%) compared to experts (47.1%), and for major CHD, experts recommended performing FE in all cases (100%) while AI recommended in majority of cases (90.9%). Discrepancies between AI and experts are detailed and reviewed. CONCLUSIONS: The evaluation found moderate agreement between AI and experts. Contextual misunderstandings and lack of specialized medical knowledge limit AI, necessitating clinical guideline guidance. Despite shortcomings, AI's referrals comprised 65% of minor CHD cases versus experts 47%, suggesting its potential as a cautious decision aid for clinicians.

Advancing Medical Practice with Artificial Intelligence: ChatGPT in Healthcare.

BACKGROUND: Advancements in artificial intelligence (AI) and natural language processing (NLP) have led to the development of language models such as ChatGPT. These models have the potential to transform healthcare and medical research. However, understanding their applications and limitations is essential. OBJECTIVES: To present a view of ChatGPT research and to critically assess ChatGPT's role in medical writing and clinical environments. METHODS: We performed a literature review via the PubMed search engine from 20 November 2022, to 23 April 2023. The search terms included ChatGPT, OpenAI, and large language models. We included studies that focused on ChatGPT, explored its use or implications in medicine, and were original research articles. The selected studies were analyzed considering study design, NLP tasks, main findings, and limitations. RESULTS: Our study included 27 articles that examined ChatGPT's performance in various tasks and medical fields. These studies covered knowledge assessment, writing, and analysis tasks. While ChatGPT was found to be useful in tasks such as generating research ideas, aiding clinical reasoning, and streamlining workflows, limitations were also identified. These limitations included inaccuracies, inconsistencies, fictitious information, and limited knowledge, highlighting the need for further improvements. CONCLUSIONS: The review underscores ChatGPT's potential in various medical applications. Yet, it also points to limitations that require careful human oversight and responsible use to improve patient care, education, and decision-making.

Prevalence of common and rare ophthalmic findings in adults attending a medical survey institute.

PURPOSE: To examine the ophthalmic data from a large database of people attending a general medical survey institute, and to investigate ophthalmic findings of the eye and its adnexa, including differences in age and sex. METHODS: Retrospective analysis including medical data of all consecutive individuals whose ophthalmic data and the prevalences of ocular pathologies were extracted from a very large database of subjects examined at a single general medical survey institute. RESULTS: Data were derived from 184,589 visits of 3676 patients (mean age 52 years, 68% males). The prevalence of the following eye pathologies were extracted. Eyelids: blepharitis (n = 4885, 13.3%), dermatochalasis (n = 4666, 12.7%), ptosis (n = 677, 1.8%), ectropion (n = 73, 0.2%), and xanthelasma (n = 160, 0.4%). Anterior segment: pinguecula (n = 3368, 9.2%), pterygium (n = 852, 2.3%), and cataract or pseudophakia (n = 9381, 27.1%). Cataract type (percentage of all phakic patients): nuclear sclerosis (n = 8908, 24.2%), posterior subcapsular (n = 846, 2.3%), and capsular anterior (n = 781, 2.1%). Pseudophakia was recorded for 697 patients (4.6%), and posterior subcapsular opacification for 229 (0.6%) patients. Optic nerve head (ONH): peripapillary atrophy (n = 4947, 13.5%), tilted disc (n = 3344, 9.1%), temporal slope (n = 410, 1.1%), ONH notch (n = 61, 0.2%), myelinated nerve fiber layer (n = 94, 0.3%), ONH drusen (n = 37, 0.1%), optic pit (n = 3, 0.0%), and ON coloboma (n = 4, 0.0%). Most pathologies were more common in males except for ONH, and most pathologies demonstrated a higher prevalence with increasing age. CONCLUSIONS: Normal ophthalmic data and the prevalences of ocular pathologies were extracted from a very large database of subjects seen at a single medical survey institute.

The role of FDG PET/CT radiomics in the prediction of pathological response to neoadjuvant treatment in patients with esophageal cancer.

Background: Attainment of a complete histopathological response following neoadjuvant therapy has been associated with favorable long-term survival outcomes in esophageal cancer patients. We investigated the ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomic features to predict the pathological response to neoadjuvant treatment in patients with esophageal cancer. Materials and methods: A retrospective review of medical records of patients with locally advanced resectable esophageal or esophagogastric junctional cancers. Included patients had a baseline FDG PET/CT scan and underwent Chemoradiotherapy for Oesophageal Cancer Followed by Surgery Study (CROSS) protocol followed by surgery. Four demographic variables and 107 PET radiomic features were extracted and analyzed using univariate and multivariate analyses to predict response to neoadjuvant therapy. Results: Overall, 53 FDG-avid primary esophageal cancer lesions were segmented and radiomic features were extracted. Seventeen radiomic features and 2 non-radiomics variables were found to exhibit significant differences between neoadjuvant therapy responders and non-responders. An unsupervised hierarchical clustering analysis using these 19 variables classified patients in a manner significantly associated with response to neoadjuvant treatment (p < 0.01). Conclusion: Our findings highlight the potential of FDG PET/CT radiomic features as a predictor for the response to neoadjuvant therapy in esophageal cancer patients. The combination of these radiomic features with select non-radiomic variables provides a model for stratifying patients based on their likelihood to respond to neoadjuvant treatment.

Towards AI-Augmented Clinical Decision-Making: An Examination of ChatGPT's Utility in Acute Ulcerative Colitis Presentations.

This study explores the potential of OpenAI's ChatGPT as a decision support tool for acute ulcerative colitis presentations in the setting of an emergency department. We assessed ChatGPT's performance in determining disease severity using TrueLove and Witts criteria and the necessity of hospitalization for patients with ulcerative colitis, comparing results with those of expert gastroenterologists. Of 20 cases, ChatGPT's assessments were found to be 80% consistent with gastroenterologist evaluations and indicated a high degree of reliability. This suggests that ChatGPT could provide as a clinical decision support tool in assessing acute ulcerative colitis, serving as an adjunct to clinical judgment.

50 Years of Pediatric Hemostasis: Knowledge, Diagnosis, and Treatment.

Studies from the past 50 years have contributed to the expanding knowledge regarding developmental hemostasis. This is a dynamic process that begins in the fetal phase and is characterized by physiological variations in platelet counts and function, and concentrations of most coagulation factors and the native coagulation inhibitors in early life, as compared with adulthood. The developmental hemostasis studies since the 1980 to 1990s established the laboratory reference values for coagulation factors. It was only a decade or two later, that thromboelastography (TEG) or (rotational thromboelastometry [ROTEM]) as well as thrombin generation studies, provided special pediatric reference values along with the ability to evaluate clot formation and lysis. In addition, global whole blood-based clotting assays provided point of care guidance for proper transfusion support to children hospitalized in intensive care units or undergoing surgery. Although uncommon, thrombosis in children and neonates is gaining increasing recognition, typically as a secondary complication in sick children. Bleeding in children, and particularly intracerebral hemorrhage in newborns, still represent a therapeutic challenge. Notably, our review will outline the advancements in understanding developmental hemostasis and its manifestations, with respect to the pathophysiology of thrombosis and bleeding complications in young children. The changes of transfusion policy and approach to thrombophilia testing during the last decade will be mentioned. Subsequently, a brief summary of the data on anticoagulant treatments in pediatric patients will be presented. Finally, we will point out the 10 most cited articles in the field of pediatric and neonatal hemostasis.

A systematic review of adeno-associated virus gene therapy clinical trials for HIV - A potential solution for patients with haemophilia and HIV?

INTRODUCTION: In the past HIV infection was a common complication of haemophilia therapy. Gene therapy trials in Haemophilia patients using rAAV have shown promising results; Unfortunately, the majority of gene therapy trials studies have excluded HIV positive patients. We decided to systematically review the published clinical trials using rAAV for HIV prevention. METHODS: A comprehensive literature search was performed to identify studies evaluating clinical trials using rAAV for HIV. The search was conducted using the MEDLINE/PubMed databases. Search keywords included 'gene therapy', 'adeno-associated virus', 'HIV' and 'clinical trial'. RESULTS: Three studies met our inclusion criteria. Two were phase 1 studies and one was a phase 2 study. One study examined an AAV coding for human monoclonal IgG1 antibody whereas the other two studies delivered a vector coding for viral protease and part of reverse transcriptase. All studies administered the vaccine intramuscularly and showed a response as well a good safety profile. DISCUSSION: The concept of using a viral vector to prevent a viral infection is revolutionary. Due to the paucity of information regarding application of any gene therapy in HIV patients and the potential use of gene therapy in haemophilia patients with HIV in the future warrants attention.

Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy.

OBJECTIVE: This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring. DATA SOURCES: A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms. STUDY ELIGIBILITY CRITERIA: Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916). METHODS: Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist. RESULTS: Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias. CONCLUSION: The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.

Cardiovascular Risk Factors and Physical Fitness Among Subjects with Asymptomatic Colonic Diverticulosis.

BACKGROUND: The association between diverticular disease and atherosclerotic cardiovascular disease (ASCVD) has been demonstrated previously, mainly in symptomatic subjects. AIMS: To evaluate 10 years cardiovascular risk, exercise performance and association to ASCVD among subjects with asymptomatic diverticulosis. METHODS: A retrospective cross-sectional cohort of self-referred participants in a medical screening program, who underwent a screening colonoscopy. Demographics, clinical and laboratory variables, ASCVD score, and metabolic equivalents (METs) during treadmill stress test were compared between subjects with and without diverticulosis as diagnosed on screening colonoscopy. RESULTS: 4586 participants underwent screening colonoscopy; 799 (17.4%) had diverticulosis. Among 50-69 yo participants, diverticulosis subjects had a higher ASCVD score compared to non-diverticulosis subjects. Exercise performance was comparable between the groups, across all age groups. Using logistic regression analysis, advanced age group (50-59 yo Adjusted odds ratio (AOR) [95% confidence interval (CI)] 2.57 (1.52-4.34), p < 0.001; 60-69 yo, AOR 2.87 (2.09-3.95), p < 0.001; ≥ 70 yo AOR 4.81 (3.23-7.15), p < 0.001; compared to < 50 yo age group), smoking [AOR 1.27 (1.05-1.55), p = 0.016], HTN [AOR 1.27 (1.03-1.56), p = 0.022], obesity [AOR 1.36 (1.06-1.74), p = 0.014] and male sex [AOR 1.29 (1.02-1.64), p = 0.036] were associated with diverticular detection during screening colonoscopy. Among males, achieving METs score ≥ 10 was inversely associated with diverticular detection during screening colonoscopy [AOR 0.64 (0.43-0.95), p = 0.027]. CONCLUSIONS: Ten years probability for ASCVD estimated by the ASCVD score is higher among subjects with asymptomatic diverticulosis compared to subjects without diverticulosis. Improved exercise performance is demonstrated for the first time to correlate with decreased probability for diverticular disease in screening colonoscopy.

Current Applications of Artificial Intelligence in Sarcoidosis.

PURPOSE: Sarcoidosis is a complex disease which can affect nearly every organ system with manifestations ranging from asymptomatic imaging findings to sudden cardiac death. As such, diagnosis and prognostication are topics of continued investigation. Recent technological advancements have introduced multiple modalities of artificial intelligence (AI) to the study of sarcoidosis. Machine learning, deep learning, and radiomics have predominantly been used to study sarcoidosis. METHODS: Articles were collected by searching online databases using keywords such as sarcoid, machine learning, artificial intelligence, radiomics, and deep learning. Article titles and abstracts were reviewed for relevance by a single reviewer. Articles written in languages other than English were excluded. CONCLUSIONS: Machine learning may be used to help diagnose pulmonary sarcoidosis and prognosticate in cardiac sarcoidosis. Deep learning is most comprehensively studied for diagnosis of pulmonary sarcoidosis and has less frequently been applied to prognostication in cardiac sarcoidosis. Radiomics has primarily been used to differentiate sarcoidosis from malignancy. To date, the use of AI in sarcoidosis is limited by the rarity of this disease, leading to small, suboptimal training sets. Nevertheless, there are applications of AI that have been used to study other systemic diseases, which may be adapted for use in sarcoidosis. These applications include discovery of new disease phenotypes, discovery of biomarkers of disease onset and activity, and treatment optimization.