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Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenoty...

Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating three-dimensional (3D) MR images is tedious and time-consuming, req...

Preoperative Delirium Risk Screening in Patients Undergoing a Cardiac Surgery: Results from the Prospective Observational FINDERI Study.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry
OBJECTIVE: Postoperative delirium (POD) is a common complication of cardiac surgery that is associated with higher morbidity, longer hospital stay, cognitive decline, and mortality. Preoperative assessments may help to identify patients´ POD risk. Ho...

Machine learning based on SEER database to predict distant metastasis of thyroid cancer.

Endocrine
OBJECTIVE: Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant metastasis or are at high risk as early as possible. This paper aimed to predict distant metastasis of...

Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations aff...

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.

Abdominal radiology (New York)
PURPOSE: To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data.

Prevalence of JC Polyomavirus in Patients with Neuroinvasive Disease of Unknown Etiology in Croatia.

Medicina (Kaunas, Lithuania)
: John Cunningham polyomavirus (JCPyV) is a highly prevalent virus in the human population. The prevalence of JCPyV in patients with central nervous system disorders has not been examined extensively. The aim of this study was to analyze the prevalen...

Automated bone age assessment in a German pediatric cohort: agreement between an artificial intelligence software and the manual Greulich and Pyle method.

European radiology
OBJECTIVES: This study aimed to evaluate the performance of artificial intelligence (AI) software in bone age (BA) assessment, according to the Greulich and Pyle (G&P) method in a German pediatric cohort.

Defining Medical AI Competencies for Medical School Graduates: Outcomes of a Delphi Survey and Medical Student/Educator Questionnaire of South Korean Medical Schools.

Academic medicine : journal of the Association of American Medical Colleges
PURPOSE: Given the increasing significance and potential impact of artificial intelligence (AI) technology on health care delivery, there is an increasing demand to integrate AI into medical school curricula. This study aimed to define medical AI com...