BACKGROUND: The global rise of metabolic associated fatty liver disease reflects the urgent need for accurate, noninvasive diagnostic approaches. The invasive nature of liver biopsy and the limited sensitivity of ultrasound in detecting early steatos...
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Jan 12, 2026
BACKGROUND: Distinguishing dangerous from benign vertigo remains a diagnostic challenge. Our study aimed to develop and evaluate a machine learning model to differentiate between dangerous and benign vertigo in the outpatient setting across two medic...
Tuberculosis (TB) remains a major global health challenge, causing approximately 1.4 million deaths annually. In many high-burden regions, limited access to expert radiological interpretation leads to delayed or missed diagnoses. To address this, we ...
Accurate identification of acute coronary syndrome (ACS) in the prehospital setting is important for timely treatments that reduce damage to the compromised myocardium. Current machine learning approaches lack sufficient performance to safely rule-in...
INTRODUCTION: After curative treatment for colorectal cancer (CRC), there is a 15% risk of recurrence. Early detection of an asymptomatic recurrence may lead to curative treatment options. To date, follow-up strategies do not have optimal sensitivity...
To investigate the potential application of existing artificial intelligence (AI) software in diagnosing COVID-19 (coronavirus disease 2019) and other pneumonia-related radiographic findings with the unprecedented challenge by COVID-19 pandemic, leve...
Gastric cancer (GC) remains a significant global health challenge with high mortality rates, often due to late-stage diagnosis. We hypothesize that Raman spectroscopy (RS) (a modern minimally invasive technique that uses light to analyze the molecula...
Biomedical physics & engineering express
Jan 6, 2026
. Accurate detection and segmentation of brain metastases (BM) from MRI are critical for the appropriate management of cancer patients. This study investigates strategies to enhance the robustness of artificial intelligence (AI)-based BM detection an...
BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection.
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