Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

作者：Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert‐Vidan, Laura Maule, Kareem A. Wahid, Mohamed A. Naser, Renjie He, Abdallah Mohamed, Clifton D. Fuller, Amy C. Moreno · 发表于：Journal of Pain and Symptom Management · 年份：2024 · DOI：10.1016/j.jpainsymman.2024.07.025 · 被引用次数：36 · 研究领域：Pain Management and Opioid Use、Pain Mechanisms and Treatments、Cancer survivorship and care

BACKGROUND/OBJECTIVES: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer. METHODS: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines. RESULTS: Forty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was al...