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Vaishya R, Dhall S, Vaish A. Artificial Intelligence (AI): A Potential Game Changer in Regenerative Orthopedics-A Scoping Review. Indian J Orthop 2024; 58:1362-1374. [PMID: 39324081 PMCID: PMC11420425 DOI: 10.1007/s43465-024-01189-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 05/21/2024] [Indexed: 09/27/2024]
Abstract
Background and Aims Regenerative orthopedics involves approaches like stem cell therapy, platelet-rich plasma (PRP) therapy, the use of biological scaffold implants, tissue engineering, etc. We aim to present a scoping review of the role of artificial intelligence (AI) in different treatment approaches of regenerative orthopedics. Methods Using the PRISMA guidelines, a search for articles for the last ten years (2013-2024) on PubMed was done, using several keywords. We have discussed the state-of-the-art, strengths/benefits, and limitations of the published research, and provide a useful resource for the way ahead in future for researchers working in this area. Results Using the eligibility criteria out of 82 initially screened publications, we included 18 studies for this review. We noticed that the treatment responses to regenerative treatments depend on several factors; hence, to facilitate better comprehensive and patient-specific treatments, AI technology is very useful. Machine learning (ML) and deep learning (DL) are a few of the most frequently used AI techniques. They use a data-driven approach for training models to make human-like decisions. Data are fed to the ML/DL algorithm and the trained model makes classifications or predictions based on its learning. Conclusion The area of regenerative orthopedics is highly sophisticated and significantly aids in providing cost-effective and non-invasive treatments to patients suffering from orthopedic ailments and injuries. Due to its promising future, the use of AI in regenerative orthopedics is an emerging and promising research field; however, its universal clinical applications are associated with some ethical considerations, which need addressing. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s43465-024-01189-1.
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Affiliation(s)
- Raju Vaishya
- Department of Orthopaedics and Joint Replacement Surgery, Indraprastha Apollo Hospitals, Sarita Vihar, New Delhi, 110076 India
| | - Sakshi Dhall
- Department of Mathematics, Jamia Millia Islamia, Delhi, 110025 India
| | - Abhishek Vaish
- Department of Orthopaedics and Joint Replacement Surgery, Indraprastha Apollo Hospitals, Sarita Vihar, New Delhi, 110076 India
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Levin JM, Lorentz SG, Hurley ET, Lee J, Throckmorton TW, Garrigues GE, MacDonald P, Anakwenze O, Schoch BS, Klifto C. Artificial intelligence in shoulder and elbow surgery: overview of current and future applications. J Shoulder Elbow Surg 2024; 33:1633-1641. [PMID: 38430978 DOI: 10.1016/j.jse.2024.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/14/2024] [Indexed: 03/05/2024]
Abstract
Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery.
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Affiliation(s)
- Jay M Levin
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA.
| | - Samuel G Lorentz
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Eoghan T Hurley
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Julia Lee
- Department of Orthopedic Surgery, Sierra Pacific Orthopedics, Fresno, CA, USA
| | - Thomas W Throckmorton
- Department of Orthopaedic Surgery, University of Tennessee-Campbell Clinic, Germantown, TN, USA
| | | | - Peter MacDonald
- Section of Orthopaedic Surgery & The Pan Am Clinic, University of Manitoba, Winnipeg, MB, Canada
| | - Oke Anakwenze
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Bradley S Schoch
- Department of Orthopedic Surgery, Mayo Clinic, Jacksonville, FL, USA
| | - Christopher Klifto
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
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Jang SJ, Rosenstadt J, Lee E, Kunze KN. Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations. Curr Rev Musculoskelet Med 2024; 17:185-206. [PMID: 38589721 DOI: 10.1007/s12178-024-09893-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/27/2024] [Indexed: 04/10/2024]
Abstract
PURPOSE OF REVIEW Patient-reported outcome measures (PROM) play a critical role in evaluating the success of treatment interventions for musculoskeletal conditions. However, predicting which patients will benefit from treatment interventions is complex and influenced by a multitude of factors. Artificial intelligence (AI) may better anticipate the propensity to achieve clinically meaningful outcomes through leveraging complex predictive analytics that allow for personalized medicine. This article provides a contemporary review of current applications of AI developed to predict clinically significant outcome (CSO) achievement after musculoskeletal treatment interventions. RECENT FINDINGS The highest volume of literature exists in the subspecialties of total joint arthroplasty, spine, and sports medicine, with only three studies identified in the remaining orthopedic subspecialties combined. Performance is widely variable across models, with most studies only reporting discrimination as a performance metric. Given the complexity inherent in predictive modeling for this task, including data availability, data handling, model architecture, and outcome selection, studies vary widely in their methodology and results. Importantly, the majority of studies have not been externally validated or demonstrate important methodological limitations, precluding their implementation into clinical settings. A substantial body of literature has accumulated demonstrating variable internal validity, limited scope, and low potential for clinical deployment. The majority of studies attempt to predict the MCID-the lowest bar of clinical achievement. Though a small proportion of models demonstrate promise and highlight the utility of AI, important methodological limitations need to be addressed moving forward to leverage AI-based applications for clinical deployment.
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Affiliation(s)
- Seong Jun Jang
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70Th Street, New York, NY, 10021, USA
| | - Jake Rosenstadt
- Georgetown University School of Medicine, Washington, DC, USA
| | - Eugenia Lee
- Weill Cornell College of Medicine, New York, NY, USA
| | - Kyle N Kunze
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70Th Street, New York, NY, 10021, USA.
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Cheng C, Liang X, Guo D, Xie D. Application of Artificial Intelligence in Shoulder Pathology. Diagnostics (Basel) 2024; 14:1091. [PMID: 38893618 PMCID: PMC11171621 DOI: 10.3390/diagnostics14111091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
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.
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Affiliation(s)
- Cong Cheng
- Department of Orthopaedics, People’s Hospital of Longhua, Shenzhen 518000, China;
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Xinzhi Liang
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Dong Guo
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Denghui Xie
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
- Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
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Martin JR, Castaneda P, Kisana H, McKee MD, Amini MH. Preoperative Patient-Reported Outcomes Predict Postoperative Clinical Outcomes Following Rotator Cuff Repair. Arthroscopy 2024; 40:1445-1452. [PMID: 37865130 DOI: 10.1016/j.arthro.2023.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/07/2023] [Accepted: 10/12/2023] [Indexed: 10/23/2023]
Abstract
PURPOSE To determine whether preoperative patient-reported outcomes (PROs) predict postoperative PROs and satisfaction following rotator cuff repair. METHODS We retrospectively identified patients who underwent a primary rotator cuff repair at a single institution. A receiver operating characteristics analysis was used to reach a preoperative American Shoulder and Elbow Surgeons (ASES) score threshold predictive of postoperative ASES and satisfaction scores. We evaluated patients above and below the receiver operating characteristics threshold by comparing their final ASES scores, ASES change (Δ) from baseline, percent maximum outcome improvement, and achievement of minimum clinically important differences, substantial clinical benefit (SCB), and patient-acceptable symptom state (PASS). Fischer exact tests were used to analyze categorical data, and continuous data were analyzed using t-test. RESULTS A total of 348 patients who underwent rotator cuff repair were included in this study. The preoperative ASES value predictive of achieving SCB was 63 (area under the curve, 0.75; 95% confidence interval: 58-67; P < .001). Patients with preoperative ASES less than 63 were significantly more likely to achieve MCID (odds ratio [OR]: 4.7, P < .001) and SCB (OR:6.1, P < .001) and had significantly higher percent maximum outcome improvement (63% vs 41%; P = 0.003) and Δ ASES scores (36 vs 12; P < .001). However, patients with preoperative ASES scores above 63 had significantly higher final ASES scores (86 vs 79; P = .003), were more likely to achieve PASS (59% vs 48%; P = .045), and had higher satisfaction scores (7.4 vs 6.7; P = .024). CONCLUSIONS Patients with high preoperative ASES scores achieve less relative improvement; however, these patients may be more likely to achieve PASS and may have higher satisfaction scores postoperatively. LEVEL OF EVIDENCE: Level III, retrospective comparative prognostic trial.
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Affiliation(s)
- John R Martin
- University of Arizona College of Medicine, Phoenix, Arizona, U.S.A
| | - Paulo Castaneda
- University of Arizona College of Medicine, Phoenix, Arizona, U.S.A
| | - Haroon Kisana
- University of Arizona College of Medicine, Phoenix, Arizona, U.S.A
| | - Michael D McKee
- University of Arizona College of Medicine, Phoenix, Arizona, U.S.A
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Bedi A, Bishop J, Keener J, Lansdown DA, Levy O, MacDonald P, Maffulli N, Oh JH, Sabesan VJ, Sanchez-Sotelo J, Williams RJ, Feeley BT. Rotator cuff tears. Nat Rev Dis Primers 2024; 10:8. [PMID: 38332156 DOI: 10.1038/s41572-024-00492-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/08/2024] [Indexed: 02/10/2024]
Abstract
Rotator cuff tears are the most common upper extremity condition seen by primary care and orthopaedic surgeons, with a spectrum ranging from tendinopathy to full-thickness tears with arthritic change. Some tears are traumatic, but most rotator cuff problems are degenerative. Not all tears are symptomatic and not all progress, and many patients in whom tears become more extensive do not experience symptom worsening. Hence, a standard algorithm for managing patients is challenging. The pathophysiology of rotator cuff tears is complex and encompasses an interplay between the tendon, bone and muscle. Rotator cuff tears begin as degenerative changes within the tendon, with matrix disorganization and inflammatory changes. Subsequently, tears progress to partial-thickness and then full-thickness tears. Muscle quality, as evidenced by the overall size of the muscle and intramuscular fatty infiltration, also influences symptoms, tear progression and the outcomes of surgery. Treatment depends primarily on symptoms, with non-operative management sufficient for most patients with rotator cuff problems. Modern arthroscopic repair techniques have improved recovery, but outcomes are still limited by a lack of understanding of how to improve tendon to bone healing in many patients.
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Affiliation(s)
- Asheesh Bedi
- Department of Orthopedic Surgery, University of Chicago, Chicago, IL, USA
- NorthShore Health System, Chicago, IL, USA
| | - Julie Bishop
- Department of Orthopedic Surgery, The Ohio State Wexner Medical Center, Columbus, OH, USA
| | - Jay Keener
- Department of Orthopedic Surgery, Washington University, St. Louis, MO, USA
| | - Drew A Lansdown
- Department of Orthopedic Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Ofer Levy
- Reading Shoulder Unit, Berkshire Independent Hospital, Reading, UK
| | - Peter MacDonald
- Department of Surgery, Max Rady College of Medicine, Winnipeg, Manitoba, Canada
| | - Nicola Maffulli
- Department of Trauma and Orthopaedic Surgery, Faculty of Medicine and Psychology, University of Rome Sapienza, Rome, Italy
| | - Joo Han Oh
- Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seoul, Korea
| | - Vani J Sabesan
- HCA Florida JFK Orthopaedic Surgery Residency Program, Atlantis Orthopedics, Atlantis, FL, USA
| | | | - Riley J Williams
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA
| | - Brian T Feeley
- Department of Orthopedic Surgery, University of California San Francisco, San Francisco, CA, USA.
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Velasquez Garcia A, Hsu KL, Marinakis K. Advancements in the diagnosis and management of rotator cuff tears. The role of artificial intelligence. J Orthop 2024; 47:87-93. [PMID: 38059047 PMCID: PMC10696306 DOI: 10.1016/j.jor.2023.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/03/2023] [Indexed: 12/08/2023] Open
Abstract
Background This review examined the role of artificial intelligence (AI) in the diagnosis and management of rotator cuff tears (RCTs). Methods A literature search was conducted in October 2023 using PubMed (MEDLINE), SCOPUS, and EMBASE databases, included only peer-reviewed studies. Relevant articles on AI technology in RCTs. A critical analysis of the relevant literature was conducted. Results AI is transforming RCTs management through faster and more precise identification and assessment using algorithms that facilitate segmentation, quantification, and classification of the RCTs across various imaging modalities. Precise algorithms focusing on preoperative factors to assess RCTs reparability have been developed for personalized treatment planning and outcome prediction. AI also aids in exercise classification and promotes patient adherence during at-home physiotherapy. Despite promising advancements, challenges in data quality and symptom integration persist. Future research should include refining AI algorithms, expanding their integration into various imaging techniques, and exploring their roles in postoperative care and surgical decision-making. Conclusions AI-driven solutions improve diagnostic accuracy and have the potential to influence treatment planning and postoperative outcomes through the automated RCTs analysis of medical imaging. Integration of high-quality datasets and clinical symptoms into AI models can enhance their reliability. Current AI algorithms can also be refined, integrated into other imaging techniques, and explored further in surgical decision-making and postoperative care.
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Affiliation(s)
- Ausberto Velasquez Garcia
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Clínica Universidad de los Andes, Department of Orthopedic Surgery, Santiago, Chile
| | - Kai-Lan Hsu
- Department of Orthopaedic Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan
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Longo UG, Di Naro C, Campisi S, Casciaro C, Bandini B, Pareek A, Bruschetta R, Pioggia G, Cerasa A, Tartarisco G. Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach. Diagnostics (Basel) 2023; 13:2915. [PMID: 37761282 PMCID: PMC10530213 DOI: 10.3390/diagnostics13182915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/30/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
AIM The overall aim of this proposal is to ameliorate the care of rotator cuff (RC) tear patients by applying an innovative machine learning approach for outcome prediction after arthroscopic repair. MATERIALS AND METHODS We applied state-of-the-art machine learning algorithms to evaluate the best predictors of the outcome, and 100 RC patients were evaluated at baseline (T0), after 1 month (T1), 3 months (T2), 6 months (T3), and 1 year (T4) from surgical intervention. The outcome measure was the Costant-Murley Shoulder Score, whereas age, sex, BMI, the 36-Item Short-Form Survey, the Simple Shoulder Test, the Hospital Anxiety and Depression Scale, the American Shoulder and Elbow Surgeons Score, the Oxford Shoulder Score, and the Shoulder Pain and Disability Index were considered as predictive factors. Support vector machine (SVM), k-nearest neighbors (k-NN), naïve Bayes (NB), and random forest (RF) algorithms were employed. RESULTS Across all sessions, the classifiers demonstrated suboptimal performance when using both the complete and shrunken sets of features. Specifically, the logistic regression (LR) classifier achieved a mean accuracy of 46.5% ± 6%, while the random forest (RF) classifier achieved 51.25% ± 4%. For the shrunken set of features, LR obtained a mean accuracy of 48.5% ± 6%, and RF achieved 45.5% ± 4.5%. No statistical differences were found when comparing the performance metrics of ML algorithms. CONCLUSIONS This study underlines the importance of extending the application of AI methods to new predictors, such as neuroimaging and kinematic data, in order to better record significant shifts in RC patients' prognosis. LIMITATIONS The data quality within the cohort could represent a limitation, since certain variables, such as smoking, diabetes, and work injury, are known to have an impact on the outcome.
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Affiliation(s)
- Umile Giuseppe Longo
- Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy; (C.D.N.); (C.C.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Calogero Di Naro
- Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy; (C.D.N.); (C.C.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Simona Campisi
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy; (S.C.); (R.B.); (G.P.); (A.C.)
- Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Carlo Casciaro
- Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy; (C.D.N.); (C.C.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Benedetta Bandini
- Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy; (C.D.N.); (C.C.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Ayoosh Pareek
- Hospital for Special Surgery, New York, NY 10021, USA;
| | - Roberta Bruschetta
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy; (S.C.); (R.B.); (G.P.); (A.C.)
- Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Giovanni Pioggia
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy; (S.C.); (R.B.); (G.P.); (A.C.)
| | - Antonio Cerasa
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy; (S.C.); (R.B.); (G.P.); (A.C.)
- S’Anna Institute, 88900 Crotone, Italy
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, 87036 Arcavacata di Rende, Italy
| | - Gennaro Tartarisco
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy; (S.C.); (R.B.); (G.P.); (A.C.)
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Rodriguez HC, Rust B, Hansen PY, Maffulli N, Gupta M, Potty AG, Gupta A. Artificial Intelligence and Machine Learning in Rotator Cuff Tears. Sports Med Arthrosc Rev 2023; 31:67-72. [PMID: 37976127 DOI: 10.1097/jsa.0000000000000371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management.
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Affiliation(s)
- Hugo C Rodriguez
- Department of Orthopaedic Surgery, Larkin Community Hospital, South Miami
- Department of Orthopaedic Surgery, Hospital for Special Surgery Florida, West Palm Beach
| | - Brandon Rust
- Nova Southeastern University, Dr. Kiran Patel College of Osteopathic Medicine, Fort Lauderdale
| | - Payton Yerke Hansen
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL
| | - Nicola Maffulli
- Department of Musculoskeletal Disorders, School of Medicine and Surgery, University of Salerno, Fisciano
- San Giovanni di Dio e Ruggi D'Aragona Hospital "Clinica Ortopedica" Department, Hospital of Salerno, Salerno, Italy
- Barts and the London School of Medicine and Dentistry, Centre for Sports and Exercise Medicine, Queen Mary University of London, London
- School of Pharmacy and Bioengineering, Keele University School of Medicine, Stoke on Trent, UK
| | - Manu Gupta
- Polar Aesthetics Dental & Cosmetic Centre, Noida, Uttar Pradesh
| | - Anish G Potty
- South Texas Orthopaedic Research Institute (STORI Inc.), Laredo, TX
| | - Ashim Gupta
- Regenerative Orthopaedics, Noida, India
- South Texas Orthopaedic Research Institute (STORI Inc.), Laredo, TX
- Future Biologics
- BioIntegrate, Lawrenceville, GA
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Guo D, Liu X, Wang D, Tang X, Qin Y. Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears. J Orthop Surg Res 2023; 18:426. [PMID: 37308995 DOI: 10.1186/s13018-023-03909-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 06/04/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experience level variability of the musculoskeletal radiologists and orthopedic surgeons. We developed a deep learning-based model for automatically diagnosing supraspinatus tears (STs) using shoulder MRI and validated its feasibility in clinical practice. MATERIALS AND METHODS A total of 701 shoulder MRI data (2804 images) were retrospectively collected for model training and internal test. An additional 69 shoulder MRIs (276 images) were collected from patients who underwent shoulder arthroplasty and constituted the surgery test set for clinical validation. Two advanced convolutional neural networks (CNN) based on Xception were trained and optimized to detect STs. The diagnostic performance of the CNN was evaluated according to its sensitivity, specificity, precision, accuracy, and F1 score. Subgroup analyses were performed to verify its robustness, and we also compared the CNN's performance with that of 4 radiologists and 4 orthopedic surgeons on the surgery and internal test sets. RESULTS Optimal diagnostic performance was achieved on the 2D model, from which F1-scores of 0.824 and 0.75, and areas under the ROC curves of 0.921 (95% confidence interval, 0.841-1.000) and 0.882 (0.817-0.947) were observed on the surgery and internal test sets. For the subgroup analysis, the 2D CNN model demonstrated a sensitivity of 0.33-1.000 and 0.625-1.000 for different degrees of tears on the surgery and internal test sets, and there was no significant performance difference between 1.5 and 3.0 T data. Compared with eight clinicians, the 2D CNN model exhibited better diagnostic performance than the junior clinicians and was equivalent to senior clinicians. CONCLUSIONS The proposed 2D CNN model realized the adequate and efficient automatic diagnoses of STs, which achieved a comparable performance of junior musculoskeletal radiologists and orthopedic surgeons. It might be conducive to assisting poor-experienced radiologists, especially in community scenarios lacking consulting experts.
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Affiliation(s)
- Deming Guo
- Orthopaedic Medical Center, The Second Hospital of Jilin University, Changchun, 130041, People's Republic of China
- Jilin Provincial Key Laboratory of Orhtopeadics, Changchun, People's Republic of China
| | - Xiaoning Liu
- Orthopaedic Medical Center, The Second Hospital of Jilin University, Changchun, 130041, People's Republic of China
| | - Dawei Wang
- Beijing Infervision Technology Co Ltd, Beijing, People's Republic of China
| | - Xiongfeng Tang
- Orthopaedic Medical Center, The Second Hospital of Jilin University, Changchun, 130041, People's Republic of China.
- Jilin Provincial Key Laboratory of Orhtopeadics, Changchun, People's Republic of China.
| | - Yanguo Qin
- Orthopaedic Medical Center, The Second Hospital of Jilin University, Changchun, 130041, People's Republic of China.
- Jilin Provincial Key Laboratory of Orhtopeadics, Changchun, People's Republic of China.
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