AIMC Topic: Adult

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Validation of an Artificial Intelligence-based Tool - The Screening Corneal Objective Risk of Ectasia Integrated into Anterion for Detection of Corneal Ectasia/Risk of Ectasia.

Middle East African journal of ophthalmology
PURPOSE: The purpose of this study was to validate the artificial intelligence-based Screening Corneal Objective Risk of Ectasia (SCORE) for the detection of corneal ectasia/risk of ectasia and to find the mean SCORE value in normal eyes.

Application of machine learning for delirium prediction and analysis of associated factors in hospitalized COVID-19 patients: A comparative study using the Korean Multidisciplinary cohort for delirium prevention (KoMCoDe).

International journal of medical informatics
BACKGROUND: The incidence of delirium in hospitalized coronavirus disease 2019 (COVID-19) patients is linked to adverse health outcomes. Predicting the occurrence and risk factors of delirium is key to preventing its sudden onset.

Universal representations in cardiovascular ECG assessment: A self-supervised learning approach.

International journal of medical informatics
BACKGROUND: The 12-lead electrocardiogram (ECG) is an established modality for cardiovascular assessment. While deep learning algorithms have shown promising results for analyzing ECG data, the limited availability of labeled datasets hinders broader...

The role of artificial intelligence literacy and innovation mindset in shaping nursing students' career and talent self-efficacy.

Nurse education in practice
BACKGROUND: As artificial intelligence (AI) transforms nursing education, AI literacy is becoming a critical necessity for increasing the career potential and talent of nursing students. Additionally, fostering an innovation mindset promotes adaptabi...

Predicting the physiological effects of multiple drugs using electronic health record.

Computers in biology and medicine
Various computational models have been developed to understand the physiological effects of drug-drug interactions, which can contribute to more effective drug treatments. However, they mostly focus on interactions of only two drugs, and do not consi...

Development and Validation of Machine Learning Model Platelet Index-based Predictor for Colorectal Cancer Stage.

Asian Pacific journal of cancer prevention : APJCP
INTRODUCTION: Colorectal cancer (CRC) staging is essential for effective treatment planning and prognosis. While platelet indices have shown promise in indicating CRC aggressiveness, a platelet index-based predictor for CRC staging has not been estab...

Enhancing Predicted Visual Acuity After SmartSight Lenticule Extraction: Identifying Key Factors With Machine Learning.

Journal of refractive surgery (Thorofare, N.J. : 1995)
PURPOSE: To develop a predictive model aimed at assessing the likelihood of improvement in corrected distance visual acuity (CDVA) for patients undergoing lenticule extraction using the SmartSight system from SCHWIND eye-tech-solutions. This model ev...

Prediction of Perceived Exertion Ratings in National Level Soccer Players Using Wearable Sensor Data and Machine Learning Techniques.

Journal of sports science & medicine
This study aimed to identify relationships between external and internal load parameters with subjective ratings of perceived exertion (RPE). Consecutively, these relationships shall be used to evaluate different machine learning models and design a ...

Impact of an AI-based laparoscopic cholecystectomy coaching program on the surgical performance: a randomized controlled trial.

International journal of surgery (London, England)
BACKGROUND: Laparoscopic cholecystectomy (LC) is the gold standard for treating symptomatic gallstones but carries inherent risks like bile duct injury. While the critical view of safety (CVS) is advocated to mitigate bile duct injury, its real-world...

Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT imaging: a multicenter study.

International journal of surgery (London, England)
INTRODUCTION: The postoperative recurrence of gastric cancer (GC) has a significant impact on the overall prognosis of patients. Therefore, accurately predicting the postoperative recurrence of GC is crucial.