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

Background: Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature. Methods: A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (shoulder OR shoulder surgery OR rotator cuff). All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both). Results: A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation. Conclusion: Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications

Venous thromboembolism following surgical management of proximal humerus fractures: a systematic review

Background: Currently, there is limited information on the incidence of venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE) after surgical treatment of proximal humerus fractures (PHFs). Therefore, the purpose of this systematic review is to evaluate the incidence of VTE, DVT, and PE following surgery for PHFs. Methods: A comprehensive search of several databases was performed from inception to May 27, 2022. Studies were screened and evaluated by 2 reviewers independently utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Only original, English studies that evaluated the incidences of VTE following surgical management of PHFs were included. Surgical procedures consisted of shoulder arthroplasty (SA) including both hemiarthroplasty (Hemi) and reverse shoulder arthroplasty (RSA) in addition to open reduction and internal fixation (ORIF). A pooled incidence for postoperative DVT, PE, and overall VTE was reported. Results: Twelve studies met the inclusion and exclusion criteria, encompassing a total of 18,238 patients. The overall DVT, PE, and VTE rates were 0.14%, 0.59%, and 0.7%, respectively. VTE was more frequently reported after SA than ORIF, (1.27% vs. 0.53%, respectively). Among SA patients, a higher rate of DVT was seen with RSA (1.2%) with the lowest DVT rate was observed for ORIF with 0.03%. Conclusions: Symptomatic VTEs following surgical treatment of PHFs, are rare, yet still relevant as a worrisome postoperative complication. Among the various procedures, VTE was the most frequently reported after SA when compared to ORIF, with RSA having the highest VTE rate

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

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