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Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, n … ted, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of n …

Radiologist Worklist Reprioritization Using Artificial Intelligence: Impact on Report Turnaround Times for CTPA Examinations Positive for Acute Pulmonary Embolism.

BACKGROUND. In patients with acute pulmonary embolism (PE), timely intervention (e.g., initiation of anticoagulation) is critical for optimizing clinical outcomes. OBJECTIVE. The purpose of this study was to evaluate the effect of artificial intelligence (AI)-based radiologist worklist reprioritization on report turnaround times for pulmonary CTA (CTPA) examinations positive for acute PE. METHODS. This retrospective single-center study included patients who underwent CTPA before (October 1, 2018-March 31, 2019 [pre-AI period]) and after (October 1, 2019-March 31, 2020 [post-AI period]) impleme…

Artificial Intelligence in Ophthalmology: A Comparative Analysis of GPT-3.5, GPT-4, and Human Expertise in Answering StatPearls Questions.

Importance Chat Generative Pre-Trained Transformer (ChatGPT) has shown promising performance in various fields, including medicine, business, and law, but its accuracy in specialty-specific medical questions, particularly in ophthalmology, is still uncertain. Purpose This study evaluates the performance of two ChatGPT models (GPT-3.5 and GPT-4) and human professionals in answering ophthalmology questions from the StatPearls question bank, assessing their outcomes, and providing insights into the integration of artificial intelligence (AI) technology in ophthalmology. Methods ChatGPT's performa…

Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy: multicenter retrospective cohort study.

BACKGROUND: The high false negative rate (FNR) associated with sentinel lymph node biopsy often leads to unnecessary axillary lymph node dissection following neoadjuvant chemotherapy (NAC) in breast cancer. The authors aimed to develop a multifactor artificial intelligence (AI) model to aid in axillary lymph node surgery. MATERIALS AND METHODS: A total of 1038 patients were enrolled, comprising 234 patients in the primary cohort, 723 patients in three external validation cohorts, and 81 patients in the prospective cohort. For predicting axillary lymph node response to NAC, robust longitudinal…

Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence-Based Tools in Critical Care: Web-Based Survey Study.

BACKGROUND: Although there is considerable interest in machine learning (ML) and artificial intelligence (AI) in critical care, the implementation of effective algorithms into practice has been limited. OBJECTIVE: We sought to understand physician perspectives of a novel intubation prediction tool. Further, we sought to understand health care provider and nonprovider perspectives on the use of ML in health care. We aim to use the data gathered to elucidate implementation barriers and determinants of this intubation prediction tool, as well as ML/AI-based algorithms in critical care and health…

Revealing the representative facial traits of different sagittal skeletal types: decipher what artificial intelligence can see by Grad-CAM.

OBJECTIVES: Aesthetic improvement is a significant concern in dental therapy. While orthodontic treatment primarily targets hard tissue, the impact on soft tissue and the extent of these changes remains empirical. This study aims to unveil the intricate relationship between facial soft tissue and skeletal types using artificial intelligence (AI) analysis. METHODS: First, we collected a dataset of 1044 3-side-photographs and categorized them based on cephalometric measurements. After pre-processing and data augmentation, samples were fed to two independent models (Sfa, Res model) for training …

A NEW ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSIS OF MAJOR PSYCHIATRIC DISEASES BASED ON VOICE ANALYSIS.

BACKGROUND: Speech features are essential components of psychiatric examinations, serving as important markers in the recognition and monitoring of mental illnesses. This study aims to develop a new clinical decision support system based on artificial intelligence, utilizing speech signals to distinguish between bipolar, depressive, anxiety and schizophrenia spectrum disorders. SUBJECTS AND METHODS: A total of 79 patients, who were admitted to the psychiatry clinic between 2020-2021, including 15 with schizophrenia spectrum disorders, 24 with anxiety disorders, 25 with depressive disorders, a…

Paving New Roads Using Allium sativum as a Repurposed Drug and Analyzing its Antiviral Action Using Artificial Intelligence Technology.

CONTEXT: The whole universe is facing a coronavirus catastrophe, and prompt treatment for the health crisis is primarily significant. The pr … light was on the activity of Allium sativum on the relegation of viral load and further highlighted artificial intelligence technology to study the attachment of the allicin compound to the SARS-CoV-2 receptor to reveal its efficacy. CONCLUSIONS: The COVID-19 pandemic has triggered interest among researchers to conduct future research on molecular docking with clinical …

Cancer care pathways across seven countries in Europe: What are the current obstacles? And how can artificial intelligence help?

BACKGROUND: Cancer poses significant challenges for healthcare professionals across the disease pathway including cancer imaging. This study constitutes part of the user requirement definition of INCISIVE EU project. The project has been designed to explore the full potential of artificial intelligence (AI)-based technologies in cancer imaging to streamline diagnosis and management. The study aimed to map cancer care pathways (breast, prostate, colorectal and lung cancers) across INCISIVE partner countries, and identify bottle necks within these pathways. METHODS: Email interviews were conduc…

Artificial intelligence-powered software detected more than half of the liver metastases overlooked by radiologists on contrast-enhanced CT.

PURPOSE: To evaluate the sensitivity of artificial intelligence (AI)-powered software in detecting liver metastases, especially those overlooked by radiologists. METHODS: Records of 746 patients diagnosed with liver metastases (November 2010-September 2017) were reviewed. Images from when radiologists first diagnosed liver metastases were reviewed, and prior contrast-enhanced CT (CECT) images were checked for availability. Two abdominal radiologists classified the lesions into overlooked lesions (all metastases missed by radiologists on prior CECT) and detected lesions (all metastases if any …

Effect of artificial intelligence-based computer-aided diagnosis on the screening outcomes of digital mammography: a matched cohort study.

OBJECTIVE: To investigate whether artificial intelligence-based computer-aided diagnosis (AI-CAD) can improve radiologists' performance when used to support radiologists' interpretation of digital mammography (DM) in breast cancer screening. METHODS: A retrospective database search identified 3158 asymptomatic Korean women who consecutively underwent screening DM between January and December 2019 without AI-CAD support, and screening DM between February and July 2020 with image interpretation aided by AI-CAD in a tertiary referral hospital using single reading. Propensity score matching was u…

Can a Novel Natural Language Processing Model and Artificial Intelligence Automatically Generate Billing Codes From Spine Surgical Operative Notes?

STUDY DESIGN: Retrospective cohort. OBJECTIVE: Billing and coding-related administrative tasks are a major source of healthcare expenditure … billing codes. As NLP models as a whole continue to improve, billing can be greatly augmented with artificial intelligence assisted generation of CPT billing codes which will help minimize error and promote standardization in the process.

Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools.

Single-cell RNA sequencing (scRNAseq) enables researchers to identify and characterize populations and subpopulations of different cell types in hearts recovering from myocardial infarction (MI) by characterizing the transcriptomes in thousands of individual cells. However, the effectiveness of the currently available tools for processing and interpreting these immense datasets is limited. We incorporated three Artificial Intelligence (AI) techniques into a toolkit for evaluating scRNAseq data: AI Autoencoding separates data from different cell types and subpopulations of cell types (cluster a…

Constructing a clinical radiographic knee osteoarthritis database using artificial intelligence tools with limited human labor: A proof of principle.

OBJECTIVE: To create a scalable and feasible retrospective consecutive knee osteoarthritis (OA) radiographic database with limited human labor using commercial and custom-built artificial intelligence (AI) tools. METHODS: We applied four AI tools, two commercially available and two custom-built tools, to analyze 6 years of clinical consecutive knee radiographs from patients aged 35-79 at the University of Copenhagen Hospital, Bispebjerg-Frederiksberg Hospital, Denmark. The tools provided Kellgren-Lawrence (KL) grades, joint space widths, patella osteophyte detection, radiographic view detecti…

Artificial intelligence-based analysis of associations between learning curve and clinical outcomes in endoscopic and microsurgical lumbar decompression surgery.

PURPOSE: A common spine surgery procedure involves decompression of the lumbar spine. The impact of the surgeon's learning curve on relevant clinical outcomes is currently not well examined in the literature. A variety of machine learning algorithms have been investigated in this study to determine how a surgeon's learning curve and other clinical parameters will influence prolonged lengths of stay (LOS), extended operating times (OT), and complications, as well as whether these clinical parameters can be reliably predicted. METHODS: A retrospective monocentric cohort study of patients with l

Prediction of pulp exposure before caries excavation using artificial intelligence: Deep learning-based image data versus standard dental radiographs.

OBJECTIVES: The objective was to examine the effect of giving Artificial Intelligence (AI)-based radiographic information versus standard radiographic and clinical information to dental students on their pulp exposure prediction ability. METHODS: 292 preoperative bitewing radiographs from patients previously treated were used. A multi-path neural network was implemented. The first path was a convolutional neural network (CNN) based on ResNet-50 architecture. The second path was a neural network trained on the distance between the pulp and lesion extracted from X-ray segmentations. Both paths …

Разработка и внедрение смартфона для диагностики трахомы: использование без кода глубокого обучения и экспозиции искусственного интеллекта.

ЦЕЛЬ: Автоматизированное машинное обучение (AutoML) позволяет врачам, не имеющим опыта кодирования, создавать свои собственные модели глубокого обучения (DL). В этом исследовании оценивается эффективность AutoML в диагностике трахомы на основе собранных на местах конъюнктурных изображений и сопоставляется с моделями DL, разработанными экспертами. МЕТОДЫ: два стажера-офтальмолога, не имеющие опыта кодирования, разработали модель AutoML с использованием общедоступного набора данных изображений, собранных на местах с помощью конъюнктурных изображений (1656 помеченных снимков). Мы разработали две

Совпадение в аннотации оригинала
PURPOSE: Automated machine learning (AutoML) allows clinicians without coding experience to build their own deep learning (DL) models. This … model design into the hands of clinicians. This approach has the potential to democratize access to artificial intelligence.

Lung CAncer SCreening in French women using low-dose CT and Artificial intelligence for DEtection: the CASCADE study protocol.

INTRODUCTION: Lung cancer screening (LCS) using low-dose CT has been demonstrated to reduce lung cancer-related mortality in large randomised controlled trials. Moving from trials to practice requires answering practical questions about the level of expertise of CT readers, the need for double reading as in trials and the potential role of artificial intelligence (AI). In addition, most LCS studies have predominantly included male participants with women being under-represented, even though the benefit of screening is greater for them. Thus, this study aims to compare the performance of a sing…