Faculty Bibliography

Accurate disease prognosis is essential for patient care but is often hindered by the lack of long-term data. This study explores deep learning training strategies that utilize large, accessible diagnostic datasets to pretrain models aimed at predicting future disease progression in knee osteoarthritis (OA), Alzheimer's disease (AD), and breast cancer (BC). While diagnostic pretraining improves prognostic task performance, naive fine-tuning for prognosis can cause 'catastrophic forgetting,' where the model's original diagnostic accuracy degrades, a significant patient safety concern in real-world settings. To address this, we propose a sequential learning strategy with experience replay. We used cohorts with knee radiographs, brain MRIs, and digital mammograms to predict 4-year structural worsening in OA, 2-year cognitive decline in AD, and 5-year cancer diagnosis in BC. Our results showed that diagnostic pretraining on larger datasets improved prognosis model performance compared to standard baselines, boosting both the Area Under the Receiver Operating Characteristic curve (AUROC) (e.g., Knee OA external: 0.77 vs 0.747; Breast Cancer: 0.874 vs 0.848) and the Area Under the Precision-Recall Curve (AUPRC) (e.g., Alzheimer's Disease: 0.752 vs 0.683). Additionally, a sequential learning approach with experience replay achieved prognostic performance comparable to dedicated single-task models (e.g., Breast Cancer AUROC 0.876 vs 0.874) while also preserving diagnostic ability. This method maintained high diagnostic accuracy (e.g., Breast Cancer Balanced Accuracy 50.4% vs 50.9% for a dedicated diagnostic model), unlike simpler multitask methods prone to catastrophic forgetting (e.g., 37.7%). Our findings show that leveraging large diagnostic datasets is a reliable and data-efficient way to enhance prognostic models while maintaining essential diagnostic skills.

BACKGROUND/UNASSIGNED:Large language models (LLMs) have shown promising abilities in text-based clinical tasks but they do not inherently interpret medical images such as knee radiographs. PURPOSE/UNASSIGNED:To develop a human-artificial intelligence interactive diagnostic approach, named radiology generative pretrained transformer (RadGPT), aimed at assisting and synergizing with human users for the interpretation of knee radiological images. MATERIALS AND METHODS/UNASSIGNED:A total of 22 512 knee roentgen ray images and reports were retrieved from Massachusetts General Hospital; 80% of these were used for model training and 10% were used for model testing and validation, respectively. Fifteen diagnostic imaging features (eg, osteoarthritis, effusion, joint space narrowing, osteophyte) were selected to label images based on their high frequency and clinical relevance in the retrieved official reports. Area under the curve scores were calculated for each feature to assess the diagnostic performance. To evaluate the quality of the generated medical text, historical clinical reports were used as the reference text. Several metrics for text generation tasks are applied, including BiLingual Evaluation Understudy, Recall-Oriented Understudy for Gisting Evaluation, Metric for Evaluation of Translation with Explicit Ordering, and Semantic Propositional Image Caption Evaluation. RESULTS/UNASSIGNED:RadGPT, in collaboration with human users, achieved area under the curve scores ranging from 0.76 for osteonecrosis to 0.91 for arthroplasty across 15 diagnostic categories for knee conditions. Compared with the baseline LLM method, RadGPT achieved higher scores, specifically 0.18 in BiLingual Evaluation Understudy score, 0.30 in Recall-Oriented Understudy for Gisting Evaluation-L, 0.10 in Metric for Evaluation of Translation with Explicit Ordering, and 0.15 in Semantic Propositional Image Caption Evaluation, which is significantly higher than the baseline LLM method, demonstrating good linguistic overlap and clinical consistency with the reference reports. CONCLUSION/UNASSIGNED:RadGPT has achieved advanced results in knee roentgen ray image feature recognition, illustrating the potential of LLMs in medical image interpretation. The study establishes a training protocol for developing artificial intelligence-assisted tools specifically focusing on the diagnosis and interpretation of knee radiological images.

BACKGROUND:The methods for predicting time-to-total knee replacement (TKR) do not provide enough information to make robust and accurate predictions. PURPOSE/OBJECTIVE:Develop and evaluate an artificial intelligence-based model for predicting time-to-TKR by analyzing longitudinal knee data and identifying key features associated with accelerated knee osteoarthritis progression. METHODS:A total of 547 subjects underwent TKR in the Osteoarthritis Initiative over nine years, and their longitudinal data was used for model training and testing. 518 and 164 subjects from Multi-Center Osteoarthritis Study and internal hospital data were used for external testing, respectively. The clinical variables, magnetic resonance (MR) images, radiographs, and quantitative and semi-quantitative assessments from images were analyzed. Deep learning (DL) models were used to extract features from radiographs and MR images. DL features were combined with clinical and image assessment features for survival analysis. A Lasso Cox feature selection method combined with a random survival forest model was used to estimate time-to-TKR. RESULTS:Utilizing only clinical variables for time-to-TKR predictions provided the estimation accuracy of 60.4% and C-index of 62.9%. Combining DL features extracted from radiographs, MR images with clinical, quantitative, and semi-quantitative image assessment features achieved the highest accuracy of 73.2%, (p=.001) and C-index of 77.3% for predicting time-to-TKR. CONCLUSIONS:The proposed predictive model demonstrated the potential of DL models and multimodal data fusion in accurately predicting time-to-TKR surgery that may help assist physicians to personalize treatment strategies and improve patient outcomes.

Musculoskeletal (MSK) imaging was among the first radiology subspecialties to adopt artificial intelligence (AI), with applications now spanning the entire MSK workflow, from image acquisition to reporting. Deep learning-based reconstruction protocols can accelerate MRI by reducing scan times and artefacts, improving accessibility in high-volume and resource-limited settings. Furthermore, AI interpretation tools have demonstrated strong performance in fracture detection, assessment of meniscal and ligament tears, bone tumour characterization and automated quantification of measurements, supporting greater diagnostic consistency across radiologists with varying experience levels. Large language models (LLMs) extend AI's impact beyond image analysis by simplifying reports for patients, automating classification systems, and streamlining clinical communication. Despite these advances, important challenges remain. Integration of AI into already established clinical workflows can be complex, and requires robust technical solutions, regulatory compliance, and strategies to maintain radiologist oversight. Questions of liability, cost-effectiveness, and the role of AI in medical education further underscore the need for careful implementation. AI is poised to fundamentally reshape MSK radiology by enhancing efficiency, improving diagnostic accuracy, and enabling more patient-centred communication. To fully realize this potential, adoption must balance innovation with safety, equity, and sustainability, ensuring AI remains a trusted assistive tool that strengthens rather than replaces radiologist expertise.

Background Deep learning (DL) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose To validate the diagnostic performance of twofold-simultaneous-multislice (SMSx2) twofold-parallel-imaging (PIx2)-accelerated DL superresolution MRI in the knee against conventional SMSx2-PIx2-accelerated MRI using arthroscopy as the reference standard. Materials and Methods Adults with painful knee conditions were prospectively enrolled from December 2021 to October 2022. Participants underwent fourfold SMSx2-PIx2-accelerated standard-of-care and investigational DL superresolution MRI at 3 T. Seven radiologists independently evaluated the MRI examinations for overall image quality (using Likert scale scores: 1, very bad, to 5, very good) and the presence or absence of meniscus and ligament tears. Articular cartilage was categorized as intact, or partial or full-thickness defects. Statistical analyses included interreader agreements (Cohen κ and Gwet AC2) and diagnostic performance testing used area under the receiver operating characteristic curve (AUC) values. Results A total of 116 adults (mean age, 45 years ± 15 [SD]; 74 men) who underwent arthroscopic surgery within 38 days ± 22 were evaluated. Overall image quality was better for DL superresolution MRI (median Likert score, 5; range, 3-5) than conventional MRI (median Likert score, 4; range, 3-5) (P < .001). Diagnostic performances of conventional versus DL superresolution MRI were similar for medial meniscus tears (AUC, 0.94 [95% CI: 0.89, 0.97] vs 0.94 [95% CI: 0.90, 0.98], respectively; P > .99), lateral meniscus tears (AUC, 0.85 [95% CI: 0.78, 0.91] vs 0.87 [95% CI: 0.81, 0.94], respectively; P = .96), and anterior cruciate ligament tears (AUC, 0.98 [95% CI: 0.93, >0.99] vs 0.98 [95% CI: 0.93, >0.99], respectively; P > .99). DL superresolution MRI (AUC, 0.78; 95% CI: 0.75, 0.81) had higher diagnostic performance than conventional MRI (AUC, 0.71; 95% CI: 0.67, 0.74; P = .002) for articular cartilage lesions. DL superresolution MRI did not introduce hallucinations or erroneously omit abnormalities. Conclusion Compared with conventional SMSx2-PIx2-accelerated MRI, fourfold SMSx2-PIx2-accelerated DL superresolution MRI in the knee provided better image quality, similar performance for detecting meniscus and ligament tears, and improved performance for depicting articular cartilage lesions. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Nevalainen in this issue.

Femoroacetabular impingement (FAI) is a cause of hip pain and can lead to hip osteoarthritis. Radiological measurements obtained from radiographs or magnetic resonance imaging (MRI) are normally used for FAI diagnosis, but they require time-consuming manual interaction, which limits accuracy and reproducibility. This study compares standard radiologic measurements against radiomics features automatically extracted from MRI for the identification of FAI patients versus healthy subjects. Three-dimensional Dixon MRI of the pelvis were retrospectively collected for 10 patients with confirmed FAI and acquired for 10 healthy subjects. The femur and acetabulum were segmented bilaterally and associated radiomics features were extracted from the four MRI contrasts of the Dixon sequence (water-only, fat-only, in-phase, and out-of-phase). A radiologist collected 21 radiological measurements typically used in FAI. The Gini importance was used to define 9 subsets with the most predictive radiomics features and one subset for the most diagnostically relevant radiological measurements. For each subset, 100 Random Forest machine learning models were trained with different data splits and fivefold cross-validation to classify healthy subjects versus FAI patients. The average performance among the 100 models was computed for each subset and compared against the performance of the radiological measurements. One model trained using the radiomics features datasets yielded 100% accuracy in the detection of FAI, whereas all other radiomics features exceeded 80% accuracy. Radiological measurements yielded 74% accuracy, consistent with previous work. The results of this preliminary work highlight for the first time the potential of radiomics for fully automated FAI diagnosis.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

BACKGROUND:parameters for an explanation as it relates to proteoglycan, collagen, and water content in these tissues. PURPOSE/OBJECTIVE:-PETRA) sequence. STUDY TYPE/METHODS:Prospective. POPULATION/METHODS:The study group consisted of 22 healthy subjects (11 females, ages: 41 ± 18 years, and 11 males, ages: 41 ± 14 years) with no known inflammation, trauma, or pain in the knee joint. FIELD STRENGTH/SEQUENCE/UNASSIGNED:-prepared 3D-PETRA sequence was used to acquire fat-suppressed images with varying spin-lock lengths (TSLs) of the knee joint at 3T. ASSESSMENT/RESULTS:parameters were measured in the anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), and patellar tendon (PT) by manually drawing ROIs over the entirety of the tissues. STATISTICAL TESTS/METHODS:parameters. Statistical significance was defined as P < 0.05. RESULTS: = 0.28-0.74) with the exception of the short fraction in the PCL (P = 0.18), and the short relaxation time in the ACL (P = 0.58) and in the PCL (P = 0.14). DATA CONCLUSION/CONCLUSIONS:parameters in three ligaments of healthy volunteers, which are thought to be related to changes in tissue composition and structure during the aging process. LEVEL OF EVIDENCE/METHODS:2 TECHNICAL EFFICACY: Stage 1.

Hamstring strain injuries (HSI) are a common occurrence in athletics and complicated by high rates of reinjury. Evidence of remaining injury observed on magnetic resonance imaging (MRI) at the time of return to sport (RTS) may be associated with strength deficits and prognostic for reinjury, however, conventional imaging has failed to establish a relationship. Quantitative measure of muscle microstructure using diffusion tensor imaging (DTI) may hold potential for assessing a possible association between injury-related structural changes and clinical outcomes. The purpose of this study was to determine the association of RTS MRI-based quantitative measures, such as edema volume, muscle volume, and DTI metrics, with clinical outcomes (i.e., strength and reinjury) following HSI. Spearman's correlations and Firth logistic regressions were used to determine relationships in between-limb imaging measures and between-limb eccentric strength and reinjury status, respectively. Twenty injuries were observed, with four reinjuries. At the time of RTS, between-limb differences in eccentric hamstring strength were significantly associated with principal effective diffusivity eigenvalue λ₁ (r = -0.64, p = 0.003) and marginally associated with mean diffusivity (r = -0.46, p = 0.056). Significant relationships between other MRI-based measures of morphology and eccentric strength were not detected, as well as between any MRI-based measure and reinjury status. In conclusion, this preliminary evidence indicates DTI may track differences in hamstring muscle microstructure, not captured by conventional imaging at the whole muscle level, that relate to eccentric strength.