| Unique ID issued by UMIN | UMIN000063315 |
|---|---|
| Receipt number | R000072467 |
| Scientific Title | Prediction of Postoperative Outcomes and Ultra-Long-Term Degeneration after Matrix-Associated Autologous Chondrocyte Implantation (MACI) using Artificial Intelligence: A Retrospective Observational Study |
| Date of disclosure of the study information | 2026/10/16 |
| Last modified on | 2026/10/16 14:34:08 |
Prediction of Postoperative Outcomes and Ultra-Long-Term Degeneration after Matrix-Associated Autologous Chondrocyte Implantation (MACI) using Artificial Intelligence
Predicting outcomes after cultured cartilage transplant surgery (MACI) using AI
Prediction of Postoperative Outcomes and Ultra-Long-Term Degeneration after Matrix-Associated Autologous Chondrocyte Implantation (MACI) using Artificial Intelligence: A Retrospective Observational Study
Postoperative outcome prediction model for cultured cartilage transplantation (MACI) using AI
| Japan |
Traumatic cartilage defect, Osteochondritis dissecanse of the knee, knee osteoarthritis
| Orthopedics |
Others
NO
The goal of this study is to build predictive models for postoperative outcomes and ultra-long-term degradation patterns using machine learning and time-series deep learning algorithms for patients who underwent autologous cultured cartilage implantation (MACI) for knee joint cartilage defects. To overcome the ceiling effects in traditional evaluation criteria, we first calculate the clinical threshold (cutoff value) of MOI (Maximal Outcome Improvement) using the anchor method from our own institution's data. Next, we apply this threshold to a large-scale post-marketing registry in Japan (RWD) to determine whether each case achieved MOI or not. Finally, by constructing a machine learning model (such as LightGBM) with 'MOI achievement or failure' as the target variable, we analyze the factors influencing MOI achievement in domestic real-world data from multiple angles. This lays the foundation for a personalized shared decision-making (SDM) support system that can suggest the optimal surgical procedure or the necessity of combined surgeries (such as high tibial osteotomy [HTO]) based on preoperative patient background.
Safety,Efficacy
MOI: Maximal Outcome Improvement
Others,meta-analysis etc
| 10 | years-old | <= |
| 70 | years-old | > |
Male and Female
Patients who underwent autologous cultured cartilage implantation: MACI for knee cartilage defects at the Orthopedic Surgery Department of Shimane University Hospital between January 1, 1996, and December 31, 2025.
Those from whom appropriate verbal consent by phone was obtained to use their medical information and interview data for this study.
Those who have clinical scores; Lysholm score, KOOS score, Forgotten Joint Score12 recorded preoperatively and postoperatively (at any time from 1 month after surgery to the final follow-up).
Those who provided answers to the anchor question in the phone survey, either "If a similar cartilage injury were found in your current knee and the doctor recommended the same 'cultured cartilage implantation,' would you want to undergo this surgery again?: Willingness for repeat surgery, primary analysis" or "Considering all daily activities, pain level, and physical function limitations, are you satisfied with your current condition? : Satisfaction, secondary analysis", or both. Patients who were aged 10 years or older and 70 years or younger at the time of surgery.
1) Those with an active joint infection in the same knee.
2) Those with severe inflammatory joint diseases, such as advanced rheumatoid arthritis.
3) Those whose follow-up data is completely missing, making it impossible to validate the AI model.
788
| 1st name | YUJI |
| Middle name | |
| Last name | UCHIO |
Shimane University, Faculty of Medicine
Department of Orthopaedic Surgery
693-8501
89-1, Enya Town, Izumo City, Shimane Prefecture
0853202240
uchio@med.shimane-u.ac.jp
| 1st name | YUJI |
| Middle name | |
| Last name | UCHIO |
Shimane University, Faculty of Medicine
Department of Orthopaedic Surgery
693-8501
89-1, Enya Town, Izumo City, Shimane Prefecture
0853202240
uchio@med.shimane-u.ac.jp
Shimane University
Self funding
Other
Shimane University Hospital, Clinical Trial Review Board
89-1, Enya Town, Izumo City, Shimane Prefecture
0853-20-2515
kenkyu@med.shimane-u.ac.jp
NO
| 2026 | Year | 10 | Month | 16 | Day |
Unpublished
Preinitiation
| 2026 | Year | 10 | Month | 14 | Day |
| 2026 | Year | 10 | Month | 14 | Day |
| 2026 | Year | 10 | Month | 19 | Day |
| 2031 | Year | 03 | Month | 31 | Day |
After getting approval from the ethics committee and permission from the head of the research institute, researchers will enter the following medical record information into a dedicated computer isolated from external access within the Department of Orthopedic Surgery at Shimane University Faculty of Medicine, and create a database (electronic survey form). We will clearly separate and manage the "existing information (preoperative data, etc.)" extracted from medical records and the "newly acquired information (postoperative scores and anchor questions)" obtained through telephone surveys.
Also, when receiving RWD data from the institution providing existing information: J-TEC Co., Ltd., the company will remove medical record IDs and names, and send data processed in a way that does not allow easy identification on our side (with study-specific ID assigned via encrypted communication or secure physical media. The collected items include all of the following: background factors: age, sex, BMI, cause, surgical factors, defect size, transplant site, cover material [periosteal flap/collagen membrane, presence of concurrent surgery [HTO, ligament reconstruction, etc., pre-op scores: Lysholm score, KOOS score, post-op scores: Lysholm score, KOOS score, FJS12 score, and complications/additional surgeries: tissue hypertrophy, joint contracture, graft detachment, additional surgery.
| 2026 | Year | 10 | Month | 16 | Day |
| 2026 | Year | 10 | Month | 16 | Day |
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000072467