https://scholar.dgist.ac.kr/handle/20.500.11750/60489 2026-10-22T09:53:31Z https://scholar.dgist.ac.kr/handle/20.500.11750/60981 Title: Artificial Intelligence-Based Prediction of Prognosis After Nonsurgical Treatment for Medial Meniscus Posterior Root Tear Author(s): Yang, Hong Yeol; Bae, Inhwan; Kim, Ji Won; Bae, Gyunghwan; Zheng, Youzhen; Kang, Sung Ju; Seon, Jong Keun Abstract: Background: Medial meniscus posterior root tear (MMPRT) is recognized as one of the leading causes of knee osteoarthritis. Given the detrimental effects of MMPRT on knee kinematics and the associated clinical consequences, substantial efforts have been directed toward improving the understanding and management of MMPRT.Purpose: To develop and validate an artificial intelligence (AI)-based prediction model for patient-specific risk assessment of clinical failure at 2 and 5 years after nonsurgical treatment of MMPRT.Study Design: Case-control study; Level of evidence, 3.Methods: The authors retrospectively reviewed a prospectively collected database of 233 patients who underwent nonsurgical treatment for MMPRT between 2006 and 2020. Patient descriptive characteristics, clinical data, and imaging variables were evaluated for their association with clinical failure, defined as conversion to total knee arthroplasty or corrective osteotomy at 2- and 5-year follow-up. Five conventional machine learning models, including Elastic Net logistic regression, multilayer perceptron, support vector machine, random forest, and Extreme Gradient Boosting, as well as a proposed deep learning model, the Grouped Graph Attention (GGAT) network, were developed and internally validated to predict clinical failure.Results: During follow-up, clinical failure occurred in 36 of 233 patients (15.5%) at 2 years and in 54 of 233 patients (23.2%) at 5 years. The deep learning-based GGAT model demonstrated better overall predictive performance compared with conventional machine learning models at both the 2- and 5-year follow-up, with test set accuracy of 0.83 to 0.91, precision of 0.87 to 0.91, sensitivity of 0.92 to 1.00, F1 score of 0.89 to 0.95, Brier score of 0.09 to 0.16, and areas under the receiver operating characteristic curve of 0.69 to 0.77. The most influential predictors of clinical failure included baseline mechanical hip-knee-ankle angle, symptom duration, body mass index, age, bone marrow edema, lateral distal femoral angle, subchondral insufficiency fracture of the knee, cartilage lesion, effusion grade, and medial proximal tibial angle.Conclusion: The deep learning-based GGAT network demonstrated accurate prediction of clinical failure at both 2 and 5 years after nonsurgical treatment of MMPRT. These findings underscore the potential value of deep learning-based risk assessment in supporting clinical decision-making and patient counseling. 2026-08-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60874 Title: Social Reasoning-Aware Trajectory Prediction via Multimodal Language Model Author(s): Bae, Inhwan; Lee, Junoh; Jeon, Hae-Gon Abstract: Recent advancements in language models have demonstrated its capacity of context understanding and generative representations. Leveraged by these developments, we propose a novel multimodal trajectory predictor based on a vision-language model, named VLMTraj, which fully takes advantage of the prior knowledge of multimodal large language models and the human-like reasoning across diverse modality information. The key idea of our model is to reframe the trajectory prediction task into a visual question answering format, using historical information as context and instructing the language model to make predictions in a conversational manner. Specifically, we transform all the inputs into a natural language style: historical trajectories are converted into text prompts, and scene images are described through image captioning. Additionally, visual features from input images are also transformed into tokens via a modality encoder and connector. The transformed data is then formatted to be used in a language model. Next, in order to guide the language model in understanding and reasoning high-level knowledge, such as scene context and social relationships between pedestrians, we introduce an auxiliary multi-task question and answers. For training, we first optimize a numerical tokenizer with the prompt data to effectively separate integer and decimal parts, allowing us to capture correlations between consecutive numbers in the language model. We then train our language model using all the visual question answering prompts. During model inference, we implement both deterministic and stochastic prediction methods through beam-search-based most-likely prediction and temperature-based multimodal generation. Our VLMTrajvalidates that the language-based model can be a powerful pedestrian trajectory predictor, and outperforms existing numerical-based predictor methods. Extensive experiments show that VLMTrajcan successfully understand social relationships and accurately extrapolate the multimodal futures on public pedestrian trajectory prediction benchmarks. 2026-05-31T15:00:00Z