Editorial Commentary: Personalized Hip Arthroscopy Outcome Prediction Using Machine Learning-The Future Is Here

Artificial Intelligence and Machine Learning May Resolve Health Care Information Overload

Biomedical information doubles almost every 2 months, and this very rate is expected to double by 2025. The result is information overload for clinicians and researchers. Today, artificial intelligence (AI) and machine learning (ML) research contribute to the deluge of information. In addition, AI large language models, although capable of automating scientific writing, are flawed. They hallucinate (make things up), are trained primarily on non-peer-reviewed content, raise ethical and legal issues, and lack human empathy. Still, when it comes to AI including ML, we are optimistic. The technology is improving rapidly. In the future, AI will help us manage unwieldy information by processing data, determining diagnoses, recommending treatments, and predicting outcomes. In research, AI and ML similarly promise efficient data analysis and literature review and will create new content in response to our instructions. Human touch will be required, and we will disclose use of AI proactively, including rationale for its use, our data input, our level of confidence in the output, and the patients or populations to whom the output may be applied. In addition, we will ensure data quality is high and bias is minimized. Most of all, we will provide essential reasoning, clinical and research guidance, and diligent oversight. Humans will remain accountable.

Re-tear after arthroscopic rotator cuff tear surgery: risk analysis using machine learning

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.

Development of Machine-Learning Algorithms to Predict Attainment of Minimal Clinically Important Difference After Hip Arthroscopy for Femoroacetabular Impingement Yield Fair Performance and Limited Clinical Utility

PURPOSE

To determine whether machine learning (ML) techniques developed using registry data could predict which patients will achieve minimum clinically important difference (MCID) on the International Hip Outcome Tool 12 (iHOT-12) patient-reported outcome measures (PROMs) after arthroscopic management of femoroacetabular impingement syndrome (FAIS). And secondly to determine which preoperative factors contribute to the predictive power of these models.

METHODS

A retrospective cohort of patients was selected from the UK's Non-Arthroplasty Hip Registry. Inclusion criteria were a diagnosis of FAIS, management via an arthroscopic procedure, and a minimum follow-up of 6 months after index surgery from August 2012 to June 2021. Exclusion criteria were for non-arthroscopic procedures and patients without FAIS. ML models were developed to predict MCID attainment. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC).

RESULTS

In total, 1,917 patients were included. The random forest, logistic regression, neural network, support vector machine, and gradient boosting models had AUROC 0.75 (0.68-0.81), 0.69 (0.63-0.76), 0.69 (0.63-0.76), 0.70 (0.64-0.77), and 0.70 (0.64-0.77), respectively. Demographic factors and disease features did not confer a high predictive performance. Baseline PROM scores alone provided comparable predictive performance to the whole dataset models. Both EuroQoL 5-Dimension 5-Level and iHOT-12 baseline scores and iHOT-12 baseline scores alone provided AUROC of 0.74 (0.68-0.80) and 0.72 (0.65-0.78), respectively, with random forest models.

CONCLUSIONS

ML models were able to predict with fair accuracy attainment of MCID on the iHOT-12 at 6-month postoperative assessment. The most successful models used all patient variables, all baseline PROMs, and baseline iHOT-12 responses. These models are not sufficiently accurate to warrant routine use in the clinic currently.

LEVEL OF EVIDENCE

Level III, retrospective cohort design; prognostic study.

Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.

Predicting Outcomes in Hip Arthroscopy for Femoroacetabular Impingement Syndrome

PURPOSE OF REVIEW

Arthroscopic treatment of femoroacetabular impingement syndrome (FAIS) continues to rise in incidence, and thus there is an increased focus on factors that predict patient outcomes. The factors that impact the outcomes of arthroscopic FAIS treatment are complex. The purpose of this review is to outline the current literature concerning predictors of patient outcomes for arthroscopic treatment of FAIS.

RECENT FINDINGS

Multiple studies have shown that various patient demographics, joint parameters, and surgical techniques are all correlated with postoperative outcomes after arthroscopic FAIS surgery, as measured by both validated patient-reported outcome (PRO) scores and rates of revision surgery including hip arthroplasty. To accurately predict patient outcomes for arthroscopic FAIS surgery, consideration should be directed toward preoperative patient-specific factors and intraoperative technical factors. The future of accurately selecting patient predictors for outcomes will only improve with increased data, improved techniques, and technological advancement.

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Background

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.

Objectives

The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?

Materials and methods

A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.

Results

Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.

Conclusion

There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.

Implications

In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

Ultrasound With Artificial Intelligence Models Predicted Palmer 1B Triangular Fibrocartilage Complex Injuries

PURPOSE

To calculate the diagnostic accuracy from the confusion matrix using deep learning (DL) on ultrasound (US) images of Palmer 1B triangular fibrocartilage complex (TFCC) injury.

METHODS

Twenty-nine wrists of 15 healthy volunteers (11 men; mean age, 34.9 years ± 9.7) (control group) and 20 wrists of 17 patients (11 men; mean age 41.0 years ± 12.2) with TFCC injury (Palmer type IB) (injury group) were included in the study. The diagnosis of Palmer 1B TFCC injury was made using magnetic resonance imaging, computed tomography arthrography, and intraoperative arthroscopic findings. In total, 2,000 images were provided to each group, 80% of which were randomly selected by AI and used as training data; the remaining data were used as test data. Transfer learning was conducted using a pretrained 3 separate models (GoogLeNet, ResNet50, ResNet101). Model evaluation was performed using a confusion matrix. The area under a receiver operating characteristic curve was also calculated. The occlusion sensitivity was used to visualize the important features.

RESULTS

For the prediction of TFCC injury by the DL model, the best score of accuracy was 0.85 in GoogLeNet, a recall was 1.0 in ResNet50 and ResNet101, and a specificity was 0.78 in GoogLeNet. In predicting the TFCC injury for the test data, the best score of the AUC was 0.97 on ResNet101. Visualization of important features showed that AI predicted the presence of injury by focusing on the morphology of the articular disc.

CONCLUSIONS

US images using the DL model predicted Palmer 1B TFCC injury with high accuracy, with the best scores of 0.85 for accuracy on GoogLeNet, 1.00 for sensitivity on ResNet50 and ResNet101, and 0.78 for specificity on GoogLeNet. The use of DL for US imaging of Palmer 1B TFCC injury predicted the injury as well as magnetic resonance imaging and computed tomography arthrography LEVEL OF EVIDENCE: IV; retrospective case series study.

Arthroscopy Annual Research Awards: Something to Celebrate

With sincere appreciation to the AANA Education Foundation for their generous support, we announce our Annual Awards for the best Clinical Research, Basic Science Research, Resident/Fellow Research, and Systematic Reviews published in 2021, as well as the Most Downloaded and Most Cited papers published 5 years ago. Also, as is customary and as we require of authors, our editors update their annual disclosures of potential conflicts of interest. Finally, we annually update our masthead, thus introducing a new Associate Editor and many new members of the Editorial Board and Social Media Board.