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Sassi M, Carnevale A, Mancuso M, Schena E, Pecchia L, Longo UG. Machine-learning models for shoulder rehabilitation exercises classification using a wearable system. Knee Surg Sports Traumatol Arthrosc 2024. [PMID: 39154254 DOI: 10.1002/ksa.12431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/26/2024] [Accepted: 07/27/2024] [Indexed: 08/19/2024]
Abstract
PURPOSE The objective of this study is to train and test machine-learning (ML) models to automatically classify shoulder rehabilitation exercises. METHODS The cohort included both healthy and patients with rotator-cuff (RC) tears. All participants performed six shoulder rehabilitation exercises, following guidelines developed by the American Society of Shoulder and Elbow Therapists. Each exercise was repeated six times, while wearing a wearable system equipped with three magneto-inertial sensors. Six supervised machine-learning models (k-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest (RF), Logistic Regression and Adaptive Boosting) were trained for the classification. The algorithms' ability to accurately classify exercise activities was evaluated using the nested cross-validation method, with different combinations of outer and inner folds. RESULTS A total of 19 healthy subjects and 17 patients with complete RC tears were enroled in the study. The highest classification performances were achieved by the RF classifier, with an accuracy of 89.91% and an F1-score of 89.89%. CONCLUSION The results of this study highlight the feasibility and effectiveness of using wearable sensors and ML algorithms to accurately classify shoulder rehabilitation exercises. These findings suggest promising prospects for implementing the proposed wearable system in remote home-based monitoring scenarios. The ease of setup and modularity of the system reduce user burden enabling patient-driven sensor positioning. LEVEL OF EVIDENCE Level III.
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Affiliation(s)
- Martina Sassi
- Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy
- Department of Engineering, Unit of Intelligent Health Technologies, Sustainable Design Management and Assessment, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Arianna Carnevale
- Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy
| | - Matilde Mancuso
- Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy
| | - Emiliano Schena
- Laboratory of Measurement and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Leandro Pecchia
- Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy
- Department of Engineering, Unit of Intelligent Health Technologies, Sustainable Design Management and Assessment, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Umile Giuseppe Longo
- Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
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Calvo E, Calvo ET. Shoulder arthroscopy: Where we come from, where we are now and what is ahead. How artificial intelligence and machine-learning technologies will transform our field. Knee Surg Sports Traumatol Arthrosc 2024; 32:1923-1925. [PMID: 38895853 DOI: 10.1002/ksa.12318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 06/21/2024]
Affiliation(s)
- Emilio Calvo
- Shoulder and Elbow Reconstructive Surgery Unit, Department of Orthopaedic Surgery and Traumatology, Hospital Universitario Fundación Jiménez Díaz, Universidad Autónoma, Madrid, Spain
| | - Elena T Calvo
- Department of Orthopaedic Surgery and Traumatology, Hospital Universitario Gregorio Marañón, Universidad Complutense, Madrid, Spain
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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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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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Shinohara I, Mifune Y, Inui A, Nishimoto H, Yoshikawa T, Kato T, Furukawa T, Tanaka S, Kusunose M, Hoshino Y, Matsushita T, Mitani M, Kuroda R. Re-tear after arthroscopic rotator cuff tear surgery: risk analysis using machine learning. J Shoulder Elbow Surg 2024; 33:815-822. [PMID: 37625694 DOI: 10.1016/j.jse.2023.07.017] [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: 04/12/2023] [Revised: 07/06/2023] [Accepted: 07/16/2023] [Indexed: 08/27/2023]
Abstract
BACKGROUND Postoperative rotator cuff retear after arthroscopic rotator cuff repair (ARCR) is still a major problem. Various risk factors such as age, gender, and tear size have been reported. Recently, magnetic resonance imaging-based stump classification was reported as an index of rotator cuff fragility. Although stump type 3 is reported to have a high retear rate, there are few reports on the risk of postoperative retear based on this classification. Machine learning (ML), an artificial intelligence technique, allows for more flexible predictive models than conventional statistical methods and has been applied to predict clinical outcomes. In this study, we used ML to predict postoperative retear risk after ARCR. METHODS The retrospective case-control study included 353 patients who underwent surgical treatment for complete rotator cuff tear using the suture-bridge technique. Patients who initially presented with retears and traumatic tears were excluded. In study participants, after the initial tear repair, rotator cuff retears were diagnosed by magnetic resonance imaging; Sugaya classification types IV and V were defined as re-tears. Age, gender, stump classification, tear size, Goutallier classification, presence of diabetes, and hyperlipidemia were used for ML parameters to predict the risk of retear. Using Python's Scikit-learn as an ML library, five different AI models (logistic regression, random forest, AdaBoost, CatBoost, LightGBM) were trained on the existing data, and the prediction models were applied to the test dataset. The performance of these ML models was measured by the area under the receiver operating characteristic curve. Additionally, key features affecting retear were evaluated. RESULTS The area under the receiver operating characteristic curve for logistic regression was 0.78, random forest 0.82, AdaBoost 0.78, CatBoost 0.83, and LightGBM 0.87, respectively for each model. LightGBM showed the highest score. The important factors for model prediction were age, stump classification, and tear size. CONCLUSIONS The ML classifier model predicted retears after ARCR with high accuracy, and the AI model showed that the most important characteristics affecting retears were age and imaging findings, including stump classification. This model may be able to predict postoperative rotator cuff retears based on clinical features.
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Affiliation(s)
- Issei Shinohara
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Yutaka Mifune
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan.
| | - Atsuyuki Inui
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Hanako Nishimoto
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Tomoya Yoshikawa
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Tatsuo Kato
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Takahiro Furukawa
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Shuya Tanaka
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Masaya Kusunose
- Department of Orthopaedic Surgery, Himeji St Mary's Hospital, Himeji, Hyogo, Japan
| | - Yuichi Hoshino
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Takehiko Matsushita
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Makoto Mitani
- Department of Orthopaedic Surgery, Himeji St Mary's Hospital, Himeji, Hyogo, Japan
| | - Ryosuke Kuroda
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
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Tu JB, Liao WJ, Liu WC, Gao XH. Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data. Sci Rep 2024; 14:5245. [PMID: 38438569 PMCID: PMC10912338 DOI: 10.1038/s41598-024-56114-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/01/2024] [Indexed: 03/06/2024] Open
Abstract
Osteoporosis is a major public health concern that significantly increases the risk of fractures. The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. A total of 10,000 complete patient records of primary healthcare data in the German Disease Analyzer database (IMS HEALTH) were included, of which 1293 diagnosed with osteoporosis and 8707 without the condition. The demographic characteristics and chronic disease data, including age, gender, lipid disorder, cancer, COPD, hypertension, heart failure, CHD, diabetes, chronic kidney disease, and stroke were collected from electronic health records. Ten different machine learning algorithms were employed to construct the predictive mode. The performance of the model was further validated and the relative importance of features in the model was analyzed. Out of the ten machine learning algorithms, the Stacker model based on Logistic Regression, AdaBoost Classifier, and Gradient Boosting Classifier demonstrated superior performance. The Stacker model demonstrated excellent performance through ten-fold cross-validation on the training set and ROC curve analysis on the test set. The confusion matrix, lift curve and calibration curves indicated that the Stacker model had optimal clinical utility. Further analysis on feature importance highlighted age, gender, lipid metabolism disorders, cancer, and COPD as the top five influential variables. In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies.
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Affiliation(s)
- Jun-Bo Tu
- Department of Orthopaedics, Xinfeng County People's Hospital, Jiangxi, 341600, Xinfeng, China
| | - Wei-Jie Liao
- Department of ICU, GanZhou People's Hospital, GanZhou, 341000, Jiangxi, China
| | - Wen-Cai Liu
- Department of Orthopaedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China.
| | - Xing-Hua Gao
- Department of Orthopaedics, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China.
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Kunze KN, Williams RJ, Ranawat AS, Pearle AD, Kelly BT, Karlsson J, Martin RK, Pareek A. Artificial intelligence (AI) and large data registries: Understanding the advantages and limitations of contemporary data sets for use in AI research. Knee Surg Sports Traumatol Arthrosc 2024; 32:13-18. [PMID: 38226678 DOI: 10.1002/ksa.12018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 11/27/2023] [Indexed: 01/17/2024]
Affiliation(s)
- Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Riley J Williams
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Anil S Ranawat
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Andrew D Pearle
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Bryan T Kelly
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Jon Karlsson
- Department of Orthopaedics, Sahlgrenska University Hospital, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - R Kyle Martin
- Department of Orthopedic Surgery, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
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de Marinis R, Marigi EM, Atwan Y, Yang L, Oeding JF, Gupta P, Pareek A, Sanchez-Sotelo J, Sperling JW. Current clinical applications of artificial intelligence in shoulder surgery: what the busy shoulder surgeon needs to know and what's coming next. JSES REVIEWS, REPORTS, AND TECHNIQUES 2023; 3:447-453. [PMID: 37928999 PMCID: PMC10625013 DOI: 10.1016/j.xrrt.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Background Artificial intelligence (AI) is a continuously expanding field with the potential to transform a variety of industries-including health care-by providing automation, efficiency, precision, accuracy, and decision-making support for simple and complex tasks. Basic knowledge of the key features as well as limitations of AI is paramount to understand current developments in this field and to successfully apply them to shoulder surgery. The purpose of the present review is to provide an overview of AI within orthopedics and shoulder surgery exploring current and forthcoming AI applications. Methods PubMed and Scopus databases were searched to provide a narrative review of the most relevant literature on AI applications in shoulder surgery. Results Despite the enormous clinical and research potential of AI, orthopedic surgery has been a relatively late adopter of AI technologies. Image evaluation, surgical planning, aiding decision-making, and facilitating patient evaluations over time are some of the current areas of development with enormous opportunities to improve surgical practice, research, and education. Furthermore, the advancement of AI-driven strategies has the potential to create a more efficient medical system that may reduce the overall cost of delivering and implementing quality health care for patients with shoulder pathology. Conclusion AI is an expanding field with the potential for broad clinical and research applications in orthopedic surgery. Many challenges still need to be addressed to fully leverage the potential of AI to clinical practice and research such as privacy issues, data ownership, and external validation of the proposed models.
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Affiliation(s)
- Rodrigo de Marinis
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Department of Orthopedic Surgery, Pontificia Universidad Católica de Chile, Santiago, Chile
- Shoulder and Elbow Unit, Hospital Dr. Sótero del Rio, Santiago, Chile
| | - Erick M. Marigi
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Yousif Atwan
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Linjun Yang
- Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, MN, USA
| | - Jacob F. Oeding
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Puneet Gupta
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA
| | | | - John W. Sperling
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
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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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