AIMC Topic: Machine Learning

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Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT.

Scientific reports
Segmentation is fundamental to medical image analysis. Recent advances in fully convolutional networks has enabled automatic segmentation; however, high labeling efforts and difficulty in acquiring sufficient and high-quality training data is still a...

Machine Learning in Prediction of Second Primary Cancer and Recurrence in Colorectal Cancer.

International journal of medical sciences
BACKGROUND: Colorectal cancer (CRC) is the third commonly diagnosed cancer worldwide. Recurrence of CRC (Re) and onset of a second primary malignancy (SPM) are important indicators in treating CRC, but it is often difficult to predict the onset of a ...

Sub-millimeter variation in human locus coeruleus is associated with dimensional measures of psychopathology: An in vivo ultra-high field 7-Tesla MRI study.

NeuroImage. Clinical
The locus coeruleus (LC) has a long-established role in the attentional and arousal response to threat, and in the emergence of pathological anxiety in pre-clinical models. However, human evidence of links between LC function and pathological anxiety...

Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.

PloS one
Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disor...

Clot Analog Attenuation in Non-contrast CT Predicts Histology: an Experimental Study Using Machine Learning.

Translational stroke research
Exact histological clot composition remains unknown. The purpose of this study was to identify the best imaging variables to be extrapolated on clot composition and clarify variability in the imaging of thrombi by non-contrast CT. Using a CT-phantom ...

Dynamic readmission prediction using routine postoperative laboratory results after radical cystectomy.

Urologic oncology
OBJECTIVE: To determine if the addition of electronic health record data enables better risk stratification and readmission prediction after radical cystectomy. Despite efforts to reduce their frequency and severity, complications and readmissions fo...

Basketball Activity Classification Based on Upper Body Kinematics and Dynamic Time Warping.

International journal of sports medicine
Basketball activity classification can help document players' statistics, allow coaches, trainers and the medical team to quantitatively supervise players' physical exertion and optimize training strategy, and further help prevent potential injuries....

Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

Diabetes/metabolism research and reviews
AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whethe...

Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques.

Scientific reports
Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algorithms to augment AKI recognition using ...

Top 10 Reviewer Critiques of Radiology Artificial Intelligence (AI) Articles: Qualitative Thematic Analysis of Reviewer Critiques of Machine Learning/Deep Learning Manuscripts Submitted to JMRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Classical machine learning (ML) and deep learning (DL) articles have rapidly captured the attention of the radiology research community and comprise an increasing proportion of articles submitted to JMRI, of variable reporting and methodo...