AIMC Topic: Humans

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Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The "ADVENTURE" study.

Anaesthesia, critical care & pain medicine
BACKGROUND: Reporting and analysis of adverse events (AE) is associated with improved health system learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the...

Development and validation of a novel colonoscopy withdrawal time indicator based on YOLOv5.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: The study aims to introduce a novel indicator, effective withdrawal time (WTS), which measures the time spent actively searching for suspicious lesions during colonoscopy and to compare WTS and the conventional withdrawal time (WT...

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.

Journal of pharmaceutical sciences
Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytic...

Human vs machine: identifying ChatGPT-generated abstracts in Gynecology and Urogynecology.

American journal of obstetrics and gynecology
BACKGROUND: ChatGPT, a publicly available artificial intelligence large language model, has allowed for sophisticated artificial intelligence technology on demand. Indeed, use of ChatGPT has already begun to make its way into medical research. Howeve...

Error detection for radiotherapy planning validation based on deep learning networks.

Journal of applied clinical medical physics
BACKGROUND: Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies...

Predictors of Disease Progression and Adverse Clinical Outcomes in Patients With Moderate Aortic Stenosis Using an Artificial Intelligence-Based Software Platform.

The American journal of cardiology
Patients with moderate aortic stenosis (AS) have a greater risk of adverse clinical outcomes than that of the general population. How this risk compares with those with severe AS, along with factors associated with outcomes and disease progression, i...

Microstrip isoelectric focusing with deep learning for simultaneous screening of diabetes, anemia, and thalassemia.

Analytica chimica acta
BACKGROUND: Hemoglobin (Hb) is an important protein in red blood cells and a crucial diagnostic indicator of diseases, e.g., diabetes, thalassemia, and anemia. However, there is a rare report on methods for the simultaneous screening of diabetes, ane...

Machine learning model for prediction of permanent stoma after anterior resection of rectal cancer: A multicenter study.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: The conversion from a temporary to a permanent stoma (PS) following rectal cancer surgery significantly impacts the quality of life of patients. However, there is currently a lack of practical preoperative tools to predict PS formation. T...

Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque.

European journal of radiology
Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given the multifaceted impact of this disease, there has been increasing interest in harnessing artificial intelligence (AI) and radiomics as complementary to...

Evaluating Deep Learning Techniques for Detecting Aneurysmal Subarachnoid Hemorrhage: A Comparative Analysis of Convolutional Neural Network and Transfer Learning Models.

World neurosurgery
OBJECTIVE: Machine learning and deep learning techniques offer a promising multidisciplinary solution for subarachnoid hemorrhage (SAH) detection. The novel transfer learning approach mitigates the time constraints associated with the traditional tec...