PMID- 36983368
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230331
IS  - 2077-0383 (Print)
IS  - 2077-0383 (Electronic)
IS  - 2077-0383 (Linking)
VI  - 12
IP  - 6
DP  - 2023 Mar 19
TI  - Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and 
      Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes.
LID - 10.3390/jcm12062369 [doi]
LID - 2369
AB  - 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.
FAU - Potty, Anish G
AU  - Potty AG
AUID- ORCID: 0000-0001-9894-1500
AD  - South Texas Orthopedic Research Institute (STORI Inc.), Laredo, TX 78045, USA.
AD  - The Institute of Musculoskeletal Excellence (TIME Orthopaedics), Laredo, TX 
      78041, USA.
AD  - School of Osteopathic Medicine, The University of the Incarnate Word, San 
      Antonio, TX 78209, USA.
FAU - Potty, Ajish S R
AU  - Potty ASR
AD  - South Texas Orthopedic Research Institute (STORI Inc.), Laredo, TX 78045, USA.
FAU - Maffulli, Nicola
AU  - Maffulli N
AUID- ORCID: 0000-0002-5327-3702
AD  - Department of Musculoskeletal Disorders, School of Medicine and Surgery, 
      University of Salerno, 84084 Fisciano, Italy.
AD  - San Giovanni di Dio e Ruggi D'Aragona Hospital "Clinica Ortopedica" Department, 
      Hospital of Salerno, 84124 Salerno, Italy.
AD  - Centre for Sports and Exercise Medicine, Barts and the London School of Medicine 
      and Dentistry, Queen Mary University of London, London E1 4DG, UK.
AD  - School of Pharmacy and Bioengineering, Keele University School of Medicine, Stoke 
      on Trent ST5 5BG, UK.
FAU - Blumenschein, Lucas A
AU  - Blumenschein LA
AD  - Department of Orthopaedics, School of Medicine, Case Western Reserve University, 
      Cleveland, OH 44106, USA.
FAU - Ganta, Deepak
AU  - Ganta D
AUID- ORCID: 0000-0001-8486-5463
AD  - School of Engineering, Texas A&M International University, Laredo, TX 78041, USA.
FAU - Mistovich, R Justin
AU  - Mistovich RJ
AD  - Department of Orthopaedics, School of Medicine, Case Western Reserve University, 
      Cleveland, OH 44106, USA.
FAU - Fuentes, Mario
AU  - Fuentes M
AD  - School of Engineering, Texas A&M International University, Laredo, TX 78041, USA.
FAU - Denard, Patrick J
AU  - Denard PJ
AUID- ORCID: 0000-0002-2641-5920
AD  - Southern Oregon Orthopedics, Medford, OR 97504, USA.
FAU - Sethi, Paul M
AU  - Sethi PM
AD  - Orthopaedic & Neurosurgery Specialists, Greenwich, CT 06905, USA.
FAU - Shah, Anup A
AU  - Shah AA
AD  - Kelsey-Seybold Clinic, Houston, TX 77584, USA.
FAU - Gupta, Ashim
AU  - Gupta A
AUID- ORCID: 0000-0003-1224-2755
AD  - South Texas Orthopedic Research Institute (STORI Inc.), Laredo, TX 78045, USA.
AD  - Future Biologics, Lawrenceville, GA 30043, USA.
AD  - BioIntegrate, Lawrenceville, GA 30043, USA.
AD  - Regenerative Orthopaedics, Noida 201301, Uttar Pradesh, India.
LA  - eng
PT  - Journal Article
DEP - 20230319
PL  - Switzerland
TA  - J Clin Med
JT  - Journal of clinical medicine
JID - 101606588
PMC - PMC10056706
OTO - NOTNLM
OT  - American Shoulder and Elbow Surgeons (ASES) score
OT  - arthroscopic rotator cuff repair
OT  - artificial intelligence
OT  - functional outcomes
OT  - machine learning
OT  - orthopaedics
OT  - predictive modelling
COIS- A.G. has industrial affiliations; however, there are no conflicts of interest 
      with the work presented in this manuscript. Thus, A.G. declares no conflicts of 
      interest. Other authors declare no conflict of interest.
EDAT- 2023/03/30 06:00
MHDA- 2023/03/30 06:01
PMCR- 2023/03/19
CRDT- 2023/03/29 01:43
PHST- 2023/02/10 00:00 [received]
PHST- 2023/03/09 00:00 [revised]
PHST- 2023/03/17 00:00 [accepted]
PHST- 2023/03/30 06:01 [medline]
PHST- 2023/03/29 01:43 [entrez]
PHST- 2023/03/30 06:00 [pubmed]
PHST- 2023/03/19 00:00 [pmc-release]
AID - jcm12062369 [pii]
AID - jcm-12-02369 [pii]
AID - 10.3390/jcm12062369 [doi]
PST - epublish
SO  - J Clin Med. 2023 Mar 19;12(6):2369. doi: 10.3390/jcm12062369.