AIMC Topic: Adult

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Robot-Assisted Endodontic Retreatment: A Case Report with Clinical Considerations.

Journal of endodontics
Fiber posts present significant challenges for nonsurgical endodontic retreatment, as improper removal may result in iatrogenic root perforation or even root fracture. Recently, robotic technology has attracted considerable attention in dentistry and...

Artificial neural networks' estimations of lower-limb kinetics in sidestepping: Comparison of full-body vs. lower-body landmark sets.

Journal of biomechanics
Artificial neural networks (ANNs) offers potential for obtaining kinetics in non-laboratory. This study compared the estimation performance for ground reaction forces (GRF) and lower-limb joint moments during sidestepping between ANNs fed with full-b...

Detecting IDH and TERTp mutations in diffuse gliomas using H-MRS with attention deep-shallow networks.

Computers in biology and medicine
BACKGROUND: Preoperative and noninvasive detection of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations in glioma is critical for prognosis and treatment planning. This study aims to develop deep lear...

Using artificial intelligence to evaluate adherence to best practices in one anastomosis gastric bypass: first steps in a real-world setting.

Surgical endoscopy
BACKGROUND: Safety in one anastomosis gastric bypass (OAGB) is judged by outcomes, but it seems reasonable to utilize best practices for safety, whose performance can be evaluated and therefore improved. We aimed to test an artificial intelligence-ba...

Preliminary investigation of an artificial intelligence-based cognitive behavioral therapy training tool.

Psychotherapy (Chicago, Ill.)
We developed an asynchronous online cognitive behavioral therapy (CBT) training tool that provides artificial intelligence- (AI-) enabled feedback to learners across eight CBT skills. We sought to evaluate the technical reliability and to ascertain h...

Beyond averaging: A transformer approach to decoding event related brain potentials.

NeuroImage
The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signa...

Development and Validation of Machine Learning-Based Model for Hospital Length of Stay in Patients Undergoing Endovascular Interventional Embolization for Intracranial Aneurysms.

World neurosurgery
OBJECTIVE: This study was to explore the factors associated with prolonged hospital length of stay (LOS) in patients with intracranial aneurysms (IAs) undergoing endovascular interventional embolization and construct prediction model machine learning...

Machine learning-based classification and prediction of typical Chinese green tea taste profiles.

Food research international (Ottawa, Ont.)
The taste of Chinese green tea is highly diverse. In this study, a combination of unsupervised and supervised learning methods was utilized to develop a model for classifying and predicting typical Chinese green tea taste. Three clustering methods we...

Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study.

BMC cancer
BACKGROUND: Accurate detection of hepatocellular carcinoma (HCC) in multiphasic contrast CT is essential for effective treatment and surgical planning. However, the variety of CT images, the misdiagnosis and missed diagnosis, and the inconsistent dia...

Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis.

Scientific reports
The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning ...