Machine learning applications and challenges in graft-versus-host disease: a scoping review.

To evaluate the characteristics of cataracts following hematopoietic stem cell transplantation (HSCT), 561 patients without any ocular complications before HSCT were reviewed. Ocular complications occurred in 196 patients (34.9%). Cataract was diagnosed in 45 of 561 patients. The 10-year estimated cataract incidence was 14%. The median onset time was 498 days after transplant (range, 38-4,535 days). The onset of cataracts was later than that of other ocular complications. Phacoemulsification was performed in 13 patients (29%) at a median of 1,028 days after HSCT (range, 375-4,549 days). In 6 of 13 patients (46%) who underwent cataract surgery, visual acuities gradually deteriorated because of the development of posterior capsule opacification. YAG (yttrium-aluminum-garnet) laser posterior capsulotomy successfully recovered their sight without any sequelae. A multivariate analysis revealed that age, acute and chronic graft versus host disease (GVHD), and systemic infection were significant risk factors for cataract, whereas neither steroid nor total body irradiation (TBI)-containing regimens affected the development of cataract. Our results showed that the clinical outcomes of cataract after HSCT are favorable and comparable to those in the general population. Hyperfractionated TBI with eye shielding may be effective for the prevention of cataract.

Predictive modeling of outcomes in acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation using machine learning techniques.

BackgroundDespite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.MethodsPUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).ResultsOf 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).ConclusionsThe ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

Mesenchymal stromal cells: heroes or non-combatants?

Metabolic dysfunction-associated steatotic liver disease (MASLD) can progress to metabolic dysfunction-associated steatohepatitis (MASH) and liver fibrosis, contributing to a heavier global health burden. Non-invasive diagnostic tools developed using machine learning (ML) and deep learning (DL), two representative artificial intelligence algorithms, are increasingly being explored for MASH and its related fibrosis assessment. This study aimed to compare the diagnostic performance of different ML and DL models and identify the top-performing models for diagnosing MASH and associated liver fibrosis. A systematic review and meta-analysis were conducted across PubMed, Web of Science, Embase and Cochrane Library from inception to May 18, 2025. Pooled area under the receiver operator characteristic curve (AUROC) values with 95 % confidence interval (CI) were calculated. Accuracy, specificity, sensitivity, positive predictive values, and negative predictive values were also recorded. Of 4,314 studies initially identified, 106 met the inclusion criteria, with 35 studies (ML: n=28; DL: n=7) providing data for analysis. Logistic Regression and Neural Network are the most commonly algorithms applied in ML and DL, respectively. The pooled AUROCs for diagnosing MASH were 0.833 (95 %CI: 0.806-0.860) for ML models and 0.841 (95 %CI: 0.782-0.900) for DL models. Light Gradient Boosting Machine (LightGBM) and ResNet50 were the best-performing models for diagnosing MASH within ML and DL algorithms, respectively, achieving corresponding AUROCs of 0.920 (95 %CI: 0.916-0.924) and 0.960 (95 %CI: 0.951-0.969). For fibrosis diagnosis, ML models had a pooled AUROC of 0.826 (95 %CI: 0.792-0.860), with Categorical Boosting (CatBoost) achieving the highest AUROC of 0.960 (95 %CI: 0.950-0.970). DL models yielded the pooled AUROC of 0.875 (95 %CI: 0.816-0.934) for fibrosis diagnosis. Both ML and DL models demonstrated strong diagnostic performance for MASH and liver fibrosis, with DL achieving marginally higher AUROCs. AI-driven approaches show promise in MASLD management.

Evaluation of multispectral imaging for freeze damage assessment in strawberries using AI-based computer vision technology

Preliminary Safety and Efficacy of Myeloablative Orca-Q with No GvHD Prophylaxis for Treatment of Advanced Hematologic Malignancies

Predictive Modeling of Hematopoietic Stem Cell Transplant Outcomes: Discovering the Role of Immunogenetic Features through Machine Learning Models

IgG Antibodies against Human Cytomegalovirus Late Protein UL94 in the Pathogenesis of Scleroderma-Like Skin Lesions in Chronic Graft Versus Host Disease

The Risk of Organ Specific Graft-Versus-Host Disease Can Be Predicted by the Multiple Single Nucleotide Polymorphism Based Predictive Models.

Prophylactic Donor Lymphocyte Infusions after Haploidentical Hematopoietic Stem Cell Transplantation for High Risk Hematological Malignancies: A Retrospective Bicentric Analysis of Serial Infusions of Increasing Doses of CD3+ Cells

Allogeneic Hematopoietic Stem Cell Transplantation in Elderly Patients: The Experience of the Spanish Group of Hematopoietic Transplantation and Cell Therapy (GETH-TC)

Positive Impact of Chronic Graft-Versus Host Disease on Survival in 928 Allogeneic Hematopoietic Stem Cell Transplant Recipients

Graft Versus Host Disease (GVHD) Prophylaxis with Everolimus and Tacrolimus Is Associated with a High Incidence of Sinusoidal Obstruction Syndrome and Microangiopathy – Results of the EVTAC Trial

Successful management of acute graft-versus-host disease with ibrutinib during cord blood transplantation for germline DDX41-mutated acute myeloid leukemia