Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm

Artificial intelligence applications in the management of musculoskeletal disorders of the shoulder: A systematic review

Purpose

The aim of the present review is to evaluate and report on the available literature discussing artificial intelligence (AI) applications to the diagnosis of shoulder conditions, outcome prediction of shoulder interventions, and the possible application of such algorithms directly to surgical procedures.

Methods

In February 2024, a search of PubMed, Cochrane and Scopus databases was performed. Studies had to evaluate AI model effectiveness for inclusion. Research on healthcare cost predictions, deterministic algorithms, patient satisfaction, protocol studies and upper-extremity fractures not involving the shoulder were excluded. The Joanna Briggs Institute Critical Appraisal tool and the Risk of Bias in Non-randomised Studies of Interventions tools were used to assess bias.

Results

Thirty-three studies were included in the analysis. Seven studies analysed the detection of rotator cuff tears (RCTs) in magnetic resonance imaging and found area under the curve (AUC) values ranged from 0.812 to 0.94 for the detection of RCTs. One study reported Area Under the Receiver Operating Characteristics values ranging from 0.79 to 0.97 for the prediction of clinical outcomes following reverse total shoulder arthroplasty. In terms of outcomes of rotator cuff repair, an AUC value ranging from 0.58 to 0.68 was reported for prediction of patient-reported outcome measures, and an AUC range of 0.87-0.92 was found for prediction of retear rate. Five studies evaluated the identification of shoulder implant models following TSA from radiographs, with reported accuracy ranging from 89.90% to 97.20%.

Conclusion

AI application enables forecasting of clinical outcomes, permits refined diagnostic evaluation and increases surgical accuracy. While promising, the translation of these technologies into routine clinical practice requires careful consideration.

Level of Evidence

Level IV.

Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review

Purpose

While several artificial intelligence (AI)-assisted medical imaging applications are reported in the recent orthopaedic literature, comparison of the clinical efficacy and utility of these applications is currently lacking. The aim of this systematic review is to evaluate the effectiveness and reliability of AI applications in orthopaedic imaging, focusing on their impact on diagnostic accuracy, image segmentation and operational efficiency across various imaging modalities.

Methods

Based on the PRISMA guidelines, a comprehensive literature search of PubMed, Cochrane and Scopus databases was performed, using combinations of keywords and MeSH descriptors ('AI', 'ML', 'deep learning', 'orthopaedic surgery' and 'imaging') from inception to March 2024. Included were studies published between September 2018 and February 2024, which evaluated machine learning (ML) model effectiveness in improving orthopaedic imaging. Studies with insufficient data regarding the output variable used to assess the reliability of the ML model, those applying deterministic algorithms, unrelated topics, protocol studies, and other systematic reviews were excluded from the final synthesis. The Joanna Briggs Institute (JBI) Critical Appraisal tool and the Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) tool were applied for the assessment of bias among the included studies.

Results

The 53 included studies reported the use of 11.990.643 images from several diagnostic instruments. A total of 39 studies reported details in terms of the Dice Similarity Coefficient (DSC), while both accuracy and sensitivity were documented across 15 studies. Precision was reported by 14, specificity by nine, and the F1 score by four of the included studies. Three studies applied the area under the curve (AUC) method to evaluate ML model performance. Among the studies included in the final synthesis, Convolutional Neural Networks (CNN) emerged as the most frequently applied category of ML models, present in 17 studies (32%).

Conclusion

The systematic review highlights the diverse application of AI in orthopaedic imaging, demonstrating the capability of various machine learning models in accurately segmenting and analysing orthopaedic images. The results indicate that AI models achieve high performance metrics across different imaging modalities. However, the current body of literature lacks comprehensive statistical analysis and randomized controlled trials, underscoring the need for further research to validate these findings in clinical settings.

Level of evidence

Systematic Review; Level of evidence IV.

Application of Artificial Intelligence in Shoulder Pathology

Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.

Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm

The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions.