Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review

Gupta, Puneet; Haeberle, Heather S.; Zimmer, Zachary R.; Levine, William N.; Williams, Riley J.; Ramkumar, Prem N.

doi:10.1016/j.xrrt.2022.12.006

Cited by 6 publications

(7 citation statements)

References 69 publications

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“…As such, there is a growing body of literature on machine learning to analyse data and answer clinical questions for both the diagnosis and prognostication of rotator cuff tears [ 3 , 4 , 5 ]. Recent reviews have demonstrated the wide range of potential applications, from the analysis of ultrasound to diagnose rotator cuff tears to the characterization of rotator cuff fatty degeneration on CT (computer tomography) scans [ 3 , 5 ]. Conversely, other studies have attempted to develop a clinical prediction tool to forecast the chance of complications versus clinical improvement following repair [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes

Potty

Potty²,

Maffulli

et al. 2023

JCM

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Machine learning (ML) has not yet been used to identify factors predictive for post-operative functional outcomes following arthroscopic rotator cuff repair (ARCR). We propose a novel algorithm to predict ARCR outcomes using machine learning. This is a retrospective cohort study from a prospectively collected database. Data were collected from the Surgical Outcome System Global Registry (Arthrex, Naples, FL, USA). Pre-operative and 3-month, 6-month, and 12-month post-operative American Shoulder and Elbow Surgeons (ASES) scores were collected and used to develop a ML model. Pre-operative factors including demography, comorbidities, cuff tear, tissue quality, and fixation implants were fed to the ML model. The algorithm then produced an expected post-operative ASES score for each patient. The ML-produced scores were compared to actual scores using standard test-train machine learning principles. Overall, 631 patients who underwent shoulder arthroscopy from January 2011 to March 2020 met inclusion criteria for final analysis. A substantial number of the test dataset predictions using the XGBoost algorithm were within the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) thresholds: 67% of the 12-month post-operative predictions were within MCID, while 84% were within SCB. Pre-operative ASES score, pre-operative pain score, body mass index (BMI), age, and tendon quality were the most important features in predicting patient recovery as identified using Shapley additive explanations (SHAP). In conclusion, the proposed novel machine learning algorithm can use pre-operative factors to predict post-operative ASES scores accurately. This can further supplement pre-operative counselling, planning, and resource allocation. Level of Evidence: III.

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Section: Introductionmentioning

confidence: 99%

“…Conversely, other studies have attempted to develop a clinical prediction tool to forecast the chance of complications versus clinical improvement following repair [ 4 ]. However, while exciting, there is much room for improvement regarding the application and accuracy of such ML models [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes

Potty

Potty²,

Maffulli

et al. 2023

JCM

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“…Most of these tasks are time-consuming processes and some of them may even be impossible for a human radiologist to conduct. In shoulder MRI, the diagnosis of rotator cuff tear and the quantification of rotator cuff muscle degeneration are common indications for applying deep learning techniques as well as imaging time acceleration [14][15][16][17][18][19][20]. Shoulder MRI typically consists of over a hundred images from various sequences and imaging planes, which takes considerable time for interpretation.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI

Lee,

Cho,

Ahn

et al. 2023

Diagnostics

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This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three musculoskeletal radiologists labeled the rotator cuff tear. The YOLO v8 rotator cuff tear detection model was then trained; training was performed with all imaging planes simultaneously and with axial, coronal, and sagittal images separately. The performances of the models were evaluated and compared using receiver operating curves and the area under the curve (AUC). The AUC was the highest when using all imaging planes (0.94; p < 0.05). Among a single imaging plane, the axial plane showed the best performance (AUC: 0.71), followed by the sagittal (AUC: 0.70) and coronal (AUC: 0.68) imaging planes. The sensitivity and accuracy were also the highest in the model with all-plane training (0.98 and 0.96, respectively). Thus, deep-learning-based automatic rotator cuff tear detection can be useful for detecting torn areas in various regions of the rotator cuff in all three imaging planes.

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“… 39 , 43 , 63 Despite the enormous clinical and research potential of AI, shoulder surgeons have been relatively late adopters of these technologies. 25 , 50 Orthopedic surgery has recently adopted AI and a variety of applications have been published over the past few years. 49 , 62 Most of the literature available on AI in orthopedics encompasses hip and knee related studies.…”

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confidence: 99%

“… 49 , 62 Most of the literature available on AI in orthopedics encompasses hip and knee related studies. 39 , 49 A recent systematic review of AI literature related to shoulder surgery 25 found 48 articles, and their external validity is yet to be determined. Being able to understand the principles behind AI represents one of the barriers to translate these techniques into our surgical practice as well as research and education efforts.…”

mentioning

confidence: 99%

Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review

Cited by 6 publications

References 69 publications

Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes

Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes

Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI

Current clinical applications of artificial intelligence in shoulder surgery: what the busy shoulder surgeon needs to know and what’s coming next

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