AIMC Topic: Treatment Outcome

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The Application of Artificial Intelligence to Enhance Spinal Cord Stimulation Efficacy for Chronic Pain Management: Current Evidence and Future Directions.

Current pain and headache reports
PURPOSE OF REVIEW: Chronic pain significantly impacts quality of life for millions globally, with spinal cord stimulation (SCS) as an established treatment for refractory chronic pain. However, traditional SCS therapies face limitations including inc...

Deep learning progressive distill for predicting clinical response to conversion therapy from preoperative CT images of advanced gastric cancer patients.

Scientific reports
Identifying patients suitable for conversion therapy through early non-invasive screening is crucial for tailoring treatment in advanced gastric cancer (AGC). This study aimed to develop and validate a deep learning method, utilizing preoperative com...

Protein Profiles Predict Treatment Responses to the PI3K Inhibitor Umbralisib in Patients with Chronic Lymphocytic Leukemia.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: The management of chronic lymphocytic leukemia (CLL) has significantly improved with targeted therapies. However, many patients experience a suboptimal response. To optimally select the best therapy, predictive biomarkers are necessary. In t...

Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.

JMIR mental health
BACKGROUND: Shame and stigma often prevent individuals with social anxiety disorder (SAD) from seeking and attending costly and time-intensive psychotherapies, highlighting the importance of brief, low-cost, and scalable treatments. Creating prescrip...

Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning.

Nature communications
Predicting treatment response is an important problem in real-world applications, where the heterogeneity of the treatment response remains a significant challenge in practice. Unsupervised machine learning methods have been proposed to address this ...

Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning.

Scientific reports
This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by integrating advanced machine learning (ML) techniques with comprehensive imaging and clinical data. ...

Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods.

Scientific reports
An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation, to their families, and healthcare providers. This study aimed at developing and validating externally patient-wise prognostic models of the global fu...

Machine learning algorithms integrating positron emission tomography/computed tomography features to predict pathological complete response after neoadjuvant chemoimmunotherapy in lung cancer.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery
OBJECTIVES: Reliable methods for predicting pathological complete response (pCR) in non-small cell lung cancer (NSCLC) patients undergoing neoadjuvant chemoimmunotherapy are still under exploration. Although Fluorine-18 fluorodeoxyglucose-positron em...