Innovative AI Tool Predicts Infection Risks from Oral Mucositis in Stem Cell Transplant Patients
A novel AI-based tool developed by University at Buffalo researchers accurately predicts the infection risks posed by oral mucositis in stem cell transplant patients, enabling better preventive care and improved outcomes.
Researchers at the University at Buffalo have developed an advanced predictive tool aimed at assessing the risk of infection caused by oral mucositis in patients undergoing stem cell transplants. Oral mucositis, characterized by painful mouth sores, significantly increases infection susceptibility, complicating recovery for patients with blood cancers receiving hematopoietic stem cell transplants (HSCT). In a comprehensive study published in the journal Cancers, the team found that patients who develop oral mucositis are nearly four times more likely to experience severe infections compared to those without the condition.
The study highlights several risk factors linked to the development of oral mucositis, including specific chemotherapy drugs such as methotrexate, high-dose chemotherapy treatments, younger age, female gender, kidney issues, and herpes simplex virus reactivation. Recognizing these factors allows clinicians to identify high-risk patients early and implement preventive measures like improved oral hygiene or cryotherapy, which uses cold temperatures to reduce inflammation.
To aid this, the researchers introduced a nomogram—a statistical tool—that integrates variables such as age, gender, race, total body irradiation, and electrolyte balance to predict the likelihood of developing severe oral mucositis. This tool simplifies complex data, enabling targeted interventions before the commencement of HSCT, potentially reducing complications and improving patient outcomes.
Furthermore, at the recent Multinational Association of Supportive Care in Cancer 2025 meeting, the team presented an explainable artificial intelligence (AI) model that enhances prediction accuracy of adverse events. Unlike traditional models, this AI system offers clear rationales behind its predictions by analyzing intricate clinical and demographic data, thus facilitating individualized treatment strategies.
These advances exemplify a multidisciplinary approach combining oncology, dentistry, and AI technology to improve supportive care in cancer treatment. As therapies like HSCT and immunotherapy become more widespread, especially among older patients, such predictive tools are vital in reducing healthcare costs, shortening hospital stays, and enhancing patient quality of life.
Source: https://medicalxpress.com/news/2025-09-tool-infection-oral-mucositis-poses.html
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