GC Green Cross Develops AI Machine Learning-Based "Joint Arthropathy Prediction Model for Hemophilia Patients"

GC Green Cross announced on November 6 that it has begun developing a "joint arthropathy prediction model for hemophilia patients" in collaboration with the Korea Hemophilia Foundation and the College of Pharmacy at Seoul National University. This project was proposed by GC Green Cross in April and gained momentum as the two institutions joined. This marks the first time a domestic company has developed a joint arthropathy prediction program aimed at improving the quality of life for hemophilia patients.

GC Green Cross Headquarters, Yongin-si, Gyeonggi-do. GC Green Cross

Hemophilia patients lack blood clotting factors, resulting in frequent joint bleeding even from minor impacts. Repeated bleeding leads to chronic joint damage, which can cause severe restrictions in mobility due to osteoporosis, osteophyte proliferation, and fibrous contracture. For this reason, the World Federation of Hemophilia (WFH) and the European Hemophilia Treatment Standardization Board (EHTSB) recommend regular monitoring of joint health.

Joint bleeding in hemophilia patients can be significantly reduced through prophylactic therapy. In particular, starting prophylaxis before the age of three greatly increases the likelihood of maintaining normal joint function. In fact, MRI results from an international study involving pediatric patients under the age of three showed a 7% incidence of joint arthropathy in the prophylaxis group, compared to 45% in the on-demand treatment group.

Furthermore, studies involving adolescent and adult patients also demonstrated that the prophylaxis group showed improvements in annual bleeding frequency, Pettersson Score, and quality of life indicators.

However, in Korea, despite approximately 70% of severe hemophilia patients experiencing joint arthropathy, the rate of prophylactic therapy implementation remains insufficient. As a result, the need for patient-tailored prediction models and systematic early management systems has been consistently raised.

GC Green Cross plans to develop a joint damage prediction model by applying AI machine learning techniques to real-world big data accumulated over about 20 years from domestic hemophilia patients.

This model will incorporate actual data from patients using hemophilia treatments such as GreenMono and GreenGene F, enabling healthcare professionals to establish optimal treatment strategies for each patient. The company stated that it aims to complete model development by next year and submit the research results to an international academic journal in the second half of the same year.

Choi Bongkyu, Head of the AID (AI & Data Science) Center at GC Green Cross, said, "Following WAPPS-HEMO, we are continuously expanding platforms to improve the quality of life for hemophilia patients in Korea," and added, "We will continue to create a patient-centered treatment environment through precision medicine utilizing AI machine learning."

Meanwhile, since 2022, GC Green Cross has been supporting hemophilia patients with the personalized software "WAPPS-HEMO," which helps determine the optimal dosage and administration interval based on each patient's individual pharmacokinetic profile.

This content was produced with the assistance of AI translation services.