AIMC Topic: Treatment Outcome

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Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble.

Scientific reports
A machine learning model was developed and validated to predict postoperative complications in patients with acute type A aortic dissection (ATAAD) who underwent total arch replacement combined with frozen elephant trunk (TAR + FET), with the goal of...

Personalized azithromycin treatment rules for children with watery diarrhea using machine learning.

Nature communications
We use machine learning to identify innovative strategies to target azithromycin to the children with watery diarrhea who are most likely to benefit. Using data from a randomized trial of azithromycin for watery diarrhea (NCT03130114), we develop per...

Treatment Response Evaluation in Prostate Cancer Using PSMA PET/CT.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
In recent years, there has been a headlong rush into the use of prostate-specific membrane antigen (PSMA)-targeted PET for the staging and restaging of men with prostate cancer (PC). To date, there have been regulatory approvals for PSMA PET for purp...

CMT-FFNet: A CMT-based feature-fusion network for predicting TACE treatment response in hepatocellular carcinoma.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurately and preoperatively predicting tumor response to transarterial chemoembolization (TACE) treatment is crucial for individualized treatment decision-making hepatocellular carcinoma (HCC). In this study, we propose a novel feature fusion netwo...

Individual muscle strengths in rehabilitation outcomes of distal radius fracture.

Journal of neuroengineering and rehabilitation
BACKGROUND: Distal radius fractures (DRFs) are common fracture types and elderly patients often struggle to achieve functional recovery, which could be overcome by precise rehabilitation. This study aims to develop an innovative approach for acquirin...

Sleep disturbances and PTSD: identifying baseline predictors of insomnia response in an intensive treatment programme.

European journal of psychotraumatology
This study examined whether baseline demographic and clinical variables could predict clinically significant reductions in insomnia symptoms among veterans receiving a 2-week Cognitive Processing Therapy (CPT)-based intensive PTSD treatment programm...

Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.

Biology direct
BACKGROUND: Pathological complete response (pCR) to neoadjuvant systemic therapy (NAST) is an established prognostic marker in breast cancer (BC). Multimodal deep learning (DL), integrating diverse data sources (radiology, pathology, omics, clinical)...

Development and validation of a machine learning model to predict cognitive behavioral therapy outcome in obsessive-compulsive disorder using clinical and neuroimaging data.

Journal of affective disorders
BACKGROUND: Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. In this study, we aimed to develop predictive models using clinical and neuroimaging data...

Multimodal deep learning for predicting unsuccessful recanalization in refractory large vessel occlusion.

European journal of radiology
PURPOSE: This study explores a multi-modal deep learning approach that integrates pre-intervention neuroimaging and clinical data to predict endovascular therapy (EVT) outcomes in acute ischemic stroke patients. To this end, consecutive stroke patien...

Neural mechanisms underlying the improvement of gait disturbances in stroke patients through robot-assisted gait training based on QEEG and fNIRS: a randomized controlled study.

Journal of neuroengineering and rehabilitation
BACKGROUND: Robot-assisted gait training is more effective in improving lower limb function and walking ability in stroke patients compared to conventional rehabilitation, but the neural mechanisms remain unclear. This study aims to explore the effec...