TY - JOUR
T1 - Machine learning applications and challenges in graft-versus-host disease
T2 - a scoping review
AU - Mushtaq, Ali Hassan
AU - Shafqat, Areez
AU - Salah, Haneen T.
AU - Hashmi, Shahrukh K.
AU - Muhsen, Ibrahim N.
N1 - Publisher Copyright:
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - Purpose of review This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. Recent findings Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, ‘‘snapshot’’ assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. Summary To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.
AB - Purpose of review This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. Recent findings Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, ‘‘snapshot’’ assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. Summary To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.
KW - allogeneic hematopoietic stem cell transplantation
KW - artificial intelligence
KW - graft versus host disease
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85175143626&partnerID=8YFLogxK
U2 - 10.1097/CCO.0000000000000996
DO - 10.1097/CCO.0000000000000996
M3 - Review article
C2 - 37820094
AN - SCOPUS:85175143626
SN - 1040-8746
VL - 35
SP - 594
EP - 600
JO - Current Opinion in Oncology
JF - Current Opinion in Oncology
IS - 6
ER -