AIMC Topic: Sensitivity and Specificity

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Deep learning and conventional hip MRI for the detection of labral and cartilage abnormalities using arthroscopy as standard of reference.

European radiology
OBJECTIVES: To evaluate the performance of high-resolution deep learning-based hip MR imaging (CSAI) compared to standard-resolution compressed sense (CS) sequences using hip arthroscopy as standard of reference.

Aptamer-functionalized graphene quantum dots combined with artificial intelligence detect bacteria for urinary tract infections.

Frontiers in cellular and infection microbiology
OBJECTIVES: Urinary tract infection is one of the most prevalent forms of bacterial infection, and prompt and efficient identification of pathogenic bacteria plays a pivotal role in the management of urinary tract infections. In this study, we propos...

Quantitative MRI radiomics approach for evaluating muscular alteration in Crohn disease: development of a machine learning-nomogram composite diagnostic tool.

Abdominal radiology (New York)
BACKGROUND: Emerging evidence underscores smooth muscle hyperplasia and hypertrophy, rather than fibrosis, as the defining characteristics of fibrostenotic lesions in Crohn disease (CD). However, non-invasive methods for quantifying these muscular ch...

Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis.

European journal of medical research
BACKGROUND: The diagnosis of idiopathic pulmonary fibrosis (IPF) is complex, which requires lung biopsy, if necessary, and multidisciplinary discussions with specialists. Clinical diagnosis of the two ailments is particularly challenging due to the i...

Rapid diagnosis of membranous nephropathy based on kidney tissue Raman spectroscopy and deep learning.

Scientific reports
Membranous nephropathy (MN) is one of the most common glomerular diseases. Although the diagnostic method based on serum PLA2R antibodies has gradually been applied in clinical practice, only 52-86% of PLA2R-associated MN patients show positive anti-...

Artificial Intelligence in CT Angiography for the Detection of Coronary Artery Stenosis and Calcified Plaque: A Systematic Review and Meta-analysis.

Academic radiology
PURPOSE: We aimed to evaluate the diagnostic performance of artificial intelligence (AI) in detecting coronary artery stenosis and calcified plaque on CT angiography (CTA), comparing its diagnostic performance with that of radiologists.

Comparison of artificial intelligence applications and commercial system performances using selected ANA IIF images.

Immunologic research
Accurate and accessible classification of anti-nuclear antibodies (ANA) through indirect immunofluorescence (IIF) imaging is crucial for diagnosing autoimmune diseases. However, many laboratories, particularly those with limited resources, lack acces...

Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection.

Nature communications
Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. Th...

Artificial intelligence in magnetic resonance imaging for predicting lymph node metastasis in rectal cancer patients: a meta-analysis.

European radiology
OBJECTIVE: This meta-analysis aims to evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based artificial intelligence (AI) in the preoperative detection of lymph node metastasis (LNM) in patients with rectal cancer and to compar...

Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG.

Translational psychiatry
Schizophrenia (SZ) and bipolar disorder (BD) pose diagnostic challenges due to overlapping clinical symptoms and genetic factors, often resulting in misdiagnosis and suboptimal treatment outcomes. This study aimed to identify EEG-based biomarkers tha...