AIMC Topic: Humans

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Machine learning-based error detection in the clinical laboratory: a critical review.

Critical reviews in clinical laboratory sciences
Laboratory test results play a crucial role in the modern medical decision-making process. As such, errors in any phase of the testing process can have substantial clinical and operational impacts. While the development of increasingly robust quality...

Predicting takotsubo syndrome subtypes: An interpretable machine learning model for differentiating emotional versus physical aetiologies.

International journal of cardiology
BACKGROUND: Takotsubo syndrome (TTS) is an acute coronary syndrome characterized by a reversible, mostly apical dysfunction of the left ventricle. Based on the triggering event, TTS has been classified as primary due to emotional causes and secondary...

RCMIX model based on pre-treatment MRI imaging predicts T-downstage in MRI-cT4 stage rectal cancer.

Cancer letters
Neoadjuvant therapy (NAT) is the standard treatment strategy for MRI-defined cT4 rectal cancer. Predicting tumor regression can guide the resection plane to some extent. Here, we covered pre-treatment MRI imaging of 363 cT4 rectal cancer patients rec...

Digital health framework for the predictive surveillance and diagnosis of atopic dermatitis.

Water research
Atopic dermatitis (AD) is an inflammatory skin disease with immunological and environmental triggers that reduces the quality of life and increases the burden on health services. It is thus important to establish effective surveillance and diagnosis ...

Artificial intelligence in muscle-invasive bladder cancer: opportunities, challenges, and clinical impact.

Current opinion in urology
PURPOSE OF REVIEW: Muscle-invasive bladder cancer (MIBC) represents an aggressive malignancy with significant morbidity and mortality. Recent advances in artificial intelligence (AI) offer promising opportunities to enhance patient care across the en...

Ultra-fast single-sequence magnetic resonance imaging (MRI) for lower back pain: diagnostic performance of a deep learning T2-Dixon pprotocol.

Clinical radiology
BACKGROUND: Conventional magnetic resonance imaging (MRI) protocols for lower back pain require multiple sequences and long acquisition times, challenging healthcare systems amid rising demand for lumbar spine imaging.

Use of machine learning for real-time antibiotic treatment adjustment in high-risk patients with CRGNB infection.

Computer methods and programs in biomedicine
BACKGROUND: Infections caused by carbapenem resistant gram-negative bacilli (CRGNB) are associated with high mortality and pose a great challenge for clinical treatment. We aim to identify patients at high risk for CRGNB as early as possible and aler...

Patient perspectives on AI in radiology: Insights from the United Arab Emirates.

Clinical imaging
RATIONALE AND OBJECTIVES: Artificial intelligence (AI) enhances diagnostic accuracy, efficiency, and patient outcomes in radiology. Patient acceptance is essential for successful integration. This study examines patient perspectives on AI in radiolog...

A plasma metabolome-derived model predicts severe liver outcomes of nonalcoholic fatty liver disease in the UK Biobank.

Diabetes, obesity & metabolism
AIMS: Severe liver disease (SLD) in nonalcoholic fatty liver disease (NAFLD) is often diagnosed late due to the long asymptomatic period of progressive fibrosis. We aimed to identify metabolomic profiles associated with SLD and develop a predictive m...