AIMC Topic: Humans

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Deep neural network (DNN) modelling for prediction of the mode of delivery.

European journal of obstetrics, gynecology, and reproductive biology
One of the factors that worry obstetricians the most is the method of delivery. In recent years, the rate of caesarean sections has steadily climbed and now exceeds the threshold advised by medical organizations. Obstetricians typically lack the tool...

Machine learning applied to gait analysis data in cerebral palsy and stroke: A systematic review.

Gait & posture
BACKGROUND: Among neurological pathologies, cerebral palsy and stroke are the main contributors to walking disorders. Machine learning methods have been proposed in the recent literature to analyze gait data from these patients. However, machine lear...

Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification.

European journal of radiology
OBJECTIVES: This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning a...

The Effect of Social Robots on Depression and Loneliness for Older Residents in Long-Term Care Facilities: A Meta-Analysis of Randomized Controlled Trials.

Journal of the American Medical Directors Association
OBJECTIVES: Depression and loneliness are challenges facing older residents living in long-term care facilities. Social robots might be a solution as nonpharmacologic interventions. The purpose of this study was to explore the effects of concrete for...

Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Underg...

Two-Stage Deep Learning Model for Diagnosis of Lumbar Spondylolisthesis Based on Lateral X-Ray Images.

World neurosurgery
BACKGROUND: Diagnosing early lumbar spondylolisthesis is challenging for many doctors because of the lack of obvious symptoms. Using deep learning (DL) models to improve the accuracy of X-ray diagnoses can effectively reduce missed and misdiagnoses i...

Artificial intelligence for detection of effusion and lipo-hemarthrosis in X-rays and CT of the knee.

European journal of radiology
BACKGROUND: Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractur...

AI in radiology: Legal responsibilities and the car paradox.

European journal of radiology
The integration of AI in radiology raises significant legal questions about responsibility for errors. Radiologists fear AI may introduce new legal challenges, despite its potential to enhance diagnostic accuracy. AI tools, even those approved by reg...

Emerging structure-based computational methods to screen the exploding accessible chemical space.

Current opinion in structural biology
Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hit...

Development of an automatic surgical planning system for high tibial osteotomy using artificial intelligence.

The Knee
BACKGROUND: This study proposed an automatic surgical planning system for high tibial osteotomy (HTO) using deep learning-based artificial intelligence and validated its accuracy. The system simulates osteotomy and measures lower-limb alignment param...