AIMC Topic: Female

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Deep learning in the precise assessment of primary Sjögren's syndrome based on ultrasound images.

Rheumatology (Oxford, England)
OBJECTIVES: This study aimed to investigate the value of a deep learning (DL) model based on greyscale ultrasound (US) images for precise assessment and accurate diagnosis of primary Sjögren's syndrome (pSS).

Prehospital triage of trauma patients: predicting major surgery using artificial intelligence as decision support.

The British journal of surgery
BACKGROUND: Matching the necessary resources and facilities to attend to the needs of trauma patients is traditionally performed by clinicians using criteria-directed triage protocols. In the present study, it was hypothesized that an artificial inte...

External validation of a machine learning-based classification algorithm for ambulatory heart rhythm diagnostics in pericardioversion atrial fibrillation patients using smartphone photoplethysmography: the SMARTBEATS-ALGO study.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: The aim of this study was to perform an external validation of an automatic machine learning (ML) algorithm for heart rhythm diagnostics using smartphone photoplethysmography (PPG) recorded by patients with atrial fibrillation (AF) and atrial f...

Identification of hypertension subtypes using microRNA profiles and machine learning.

European journal of endocrinology
OBJECTIVE: Hypertension is a major cardiovascular risk factor affecting about 1 in 3 adults. Although the majority of hypertension cases (∼90%) are classified as "primary hypertension" (PHT), endocrine hypertension (EHT) accounts for ∼10% of cases an...

Identifying responders to gabapentin for the treatment of alcohol use disorder: an exploratory machine learning approach.

Alcohol and alcoholism (Oxford, Oxfordshire)
BACKGROUND: Gabapentin, an anticonvulsant medication, has been proposed as a treatment for alcohol use disorder (AUD). A multisite study tested gabapentin enacarbil extended-release (GE-XR; 600 mg/twice a day), a prodrug formulation, combined with a ...

Development of a deep learning-based model to evaluate changes during radiotherapy using cervical cancer digital pathology.

Journal of radiation research
This study aims to create a deep learning-based classification model for cervical cancer biopsy before and during radiotherapy, visualize the results on whole slide images (WSIs), and explore the clinical significance of obtained features. This study...

Model development and validation for predicting small-cell lung cancer bone metastasis utilizing diverse machine learning algorithms based on the SEER database.

Medicine
The aim of this study was to devise a machine learning algorithm with superior performance in predicting bone metastasis (BM) in small cell lung cancer (SCLC) and create a straightforward web-based predictor based on the developed algorithm. Data com...

Preoperative prediction of textbook outcome in intrahepatic cholangiocarcinoma by interpretable machine learning: A multicenter cohort study.

World journal of gastroenterology
BACKGROUND: To investigate the preoperative factors influencing textbook outcomes (TO) in Intrahepatic cholangiocarcinoma (ICC) patients and evaluate the feasibility of an interpretable machine learning model for preoperative prediction of TO, we dev...

Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection.

World journal of gastroenterology
BACKGROUND: Colorectal polyps are precancerous diseases of colorectal cancer. Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer. Endoscopic mucosal resection (EMR) is a common polypectomy pro...

Predicting Postoperative Circulatory Complications in Older Patients: A Machine Learning Approach.

Biomedical and environmental sciences : BES
OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients.