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Artificial Intelligence in Orthopedic Surgery and Sport Medicine

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 11800

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Prof. Dr. Umile Giuseppe Longo

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Operative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy
Interests: sports traumatology; arthroscopic surgery of shoulder, knee, and ankle; replacement surgery of shoulder, knee, and hip
Special Issues, Collections and Topics in MDPI journals

Dr. Gennaro Tartarisco

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Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy
Interests: monitoring of muscle fatigue; neurodegenerative diseases; movement in patients with arthroplasty

Dr. Antonio Cerasa

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Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy
Interests: prefrontal cortex; cognitive neuroscience; brain mapping; neurology; cognitive science; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advances in artificial intelligence (AI) in orthopedic surgery and sport medicine. Although the interest in the AI algorithm for classification and diagnosis is growing, we still suffer from a lack of applications for clinicians to understand and trust the output of AI models, especially when involved in decision processes. Moreover, very little discussion has revolved around how to apply these tools for the early prediction of surgical treatments and care of patients suffering from musculoskeletal diseases.

Prof. Dr. Umile Giuseppe Longo
Dr. Gennaro Tartarisco
Dr. Antonio Cerasa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

artificial intelligence
machine learning
orthopedic imaging
decision support system

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Published Papers (7 papers)

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Research

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20 pages, 239 KiB

Open AccessArticle

Google Bard and ChatGPT in Orthopedics: Which Is the Better Doctor in Sports Medicine and Pediatric Orthopedics? The Role of AI in Patient Education

by Riccardo Giorgino, Mario Alessandri-Bonetti, Matteo Del Re, Fabio Verdoni, Giuseppe M. Peretti and Laura Mangiavini

Diagnostics 2024, 14(12), 1253; https://doi.org/10.3390/diagnostics14121253 - 13 Jun 2024

Cited by 1 | Viewed by 1214

Abstract

Background: This study evaluates the potential of ChatGPT and Google Bard as educational tools for patients in orthopedics, focusing on sports medicine and pediatric orthopedics. The aim is to compare the quality of responses provided by these natural language processing (NLP) models, addressing concerns about the potential dissemination of incorrect medical information. Methods: Ten ACL- and flat foot-related questions from a Google search were presented to ChatGPT-3.5 and Google Bard. Expert orthopedic surgeons rated the responses using the Global Quality Score (GQS). The study minimized bias by clearing chat history before each question, maintaining respondent anonymity and employing statistical analysis to compare response quality. Results: ChatGPT-3.5 and Google Bard yielded good-quality responses, with average scores of 4.1 ± 0.7 and 4 ± 0.78, respectively, for sports medicine. For pediatric orthopedics, Google Bard scored 3.5 ± 1, while the average score for responses generated by ChatGPT was 3.8 ± 0.83. In both cases, no statistically significant difference was found between the platforms (p = 0.6787, p = 0.3092). Despite ChatGPT’s responses being considered more readable, both platforms showed promise for AI-driven patient education, with no reported misinformation. Conclusions: ChatGPT and Google Bard demonstrate significant potential as supplementary patient education resources in orthopedics. However, improvements are needed for increased reliability. The study underscores the evolving role of AI in orthopedics and calls for continued research to ensure a conscientious integration of AI in healthcare education. Full article

(This article belongs to the Special Issue Artificial Intelligence in Orthopedic Surgery and Sport Medicine)

12 pages, 992 KiB

Open AccessArticle

Deep Learning-Based Prediction Model for Gait Recovery after a Spinal Cord Injury

by Hyun-Joon Yoo, Kwang-Sig Lee, Bummo Koo, Chan-Woo Yong and Chae-Won Kim

Diagnostics 2024, 14(6), 579; https://doi.org/10.3390/diagnostics14060579 - 8 Mar 2024

Cited by 1 | Viewed by 1489

Abstract

Predicting gait recovery after a spinal cord injury (SCI) during an acute rehabilitation phase is important for planning rehabilitation strategies. However, few studies have been conducted on this topic to date. In this study, we developed a deep learning-based prediction model for gait recovery after SCI upon discharge from an acute rehabilitation facility. Data were collected from 405 patients with acute SCI admitted to the acute rehabilitation facility of Korea University Anam Hospital between June 2008 and December 2022. The dependent variable was Functional Ambulation Category at the time of discharge (FAC-DC). Seventy-one independent variables were selected from the existing literature: basic information, International Standards for Neurological Classification of SCI scores, neurogenic bladders, initial FAC, and somatosensory-evoked potentials of the lower extremity. Recurrent neural network (RNN), linear regression (LR), Ridge, and Lasso methods were compared for FAC-DC prediction in terms of the root-mean-squared error (RMSE). RNN variable importance, which is the RMSE gap between a complete RNN model and an RNN model excluding a certain variable, was used to evaluate the contribution of this variable. Based on the results of this study, the performance of the RNN was far better than that of LR, Ridge, and Lasso. The respective RMSEs were 0.3738, 2.2831, 1.3161, and 1.0246 for all the participants; 0.3727, 1.7176, 1.3914, and 1.3524 for those with trauma; and 0.3728, 1.7516, 1.1012, and 0.8889 for those without trauma. In terms of RNN variable importance, lower-extremity motor strength (right and left ankle dorsiflexors, right knee extensors, and left long toe extensors) and the neurological level of injury were ranked among the top five across the boards. Therefore, initial FAC was the seventh, third, and ninth most important predictor for all participants, those with trauma, and those without trauma, respectively. In conclusion, this study developed a deep learning-based prediction model with excellent performance for gait recovery after SCI at the time of discharge from an acute rehabilitation facility. This study also demonstrated the strength of deep learning as an explainable artificial intelligence method for identifying the most important predictors. Full article

(This article belongs to the Special Issue Artificial Intelligence in Orthopedic Surgery and Sport Medicine)

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17 pages, 5885 KiB

Open AccessArticle

Artificial Intelligence Image Recognition System for Preventing Wrong-Site Upper Limb Surgery

by Yi-Chao Wu, Chao-Yun Chang, Yu-Tse Huang, Sung-Yuan Chen, Cheng-Hsuan Chen and Hsuan-Kai Kao

Diagnostics 2023, 13(24), 3667; https://doi.org/10.3390/diagnostics13243667 - 14 Dec 2023

Cited by 1 | Viewed by 1590

Abstract

Our image recognition system employs a deep learning model to differentiate between the left and right upper limbs in images, allowing doctors to determine the correct surgical position. From the experimental results, it was found that the precision rate and the recall rate of the intelligent image recognition system for preventing wrong-site upper limb surgery proposed in this paper could reach 98% and 93%, respectively. The results proved that our Artificial Intelligence Image Recognition System (AIIRS) could indeed assist orthopedic surgeons in preventing the occurrence of wrong-site left and right upper limb surgery. At the same time, in future, we will apply for an IRB based on our prototype experimental results and we will conduct the second phase of human trials. The results of this research paper are of great benefit and research value to upper limb orthopedic surgery. Full article

(This article belongs to the Special Issue Artificial Intelligence in Orthopedic Surgery and Sport Medicine)

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15 pages, 2308 KiB

Open AccessArticle

Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach

by Umile Giuseppe Longo, Calogero Di Naro, Simona Campisi, Carlo Casciaro, Benedetta Bandini, Ayoosh Pareek, Roberta Bruschetta, Giovanni Pioggia, Antonio Cerasa and Gennaro Tartarisco

Diagnostics 2023, 13(18), 2915; https://doi.org/10.3390/diagnostics13182915 - 11 Sep 2023

Viewed by 1325

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. Full article

(This article belongs to the Special Issue Artificial Intelligence in Orthopedic Surgery and Sport Medicine)

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18 pages, 2939 KiB

Open AccessArticle

Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques

by Marwa Elseddik, Reham R. Mostafa, Ahmed Elashry, Nora El-Rashidy, Shaker El-Sappagh, Shimaa Elgamal, Ahmed Aboelfetouh and Hazem El-Bakry

Diagnostics 2023, 13(3), 492; https://doi.org/10.3390/diagnostics13030492 - 29 Jan 2023

Cited by 6 | Viewed by 2534

Abstract

Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman’s correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery. Full article

(This article belongs to the Special Issue Artificial Intelligence in Orthopedic Surgery and Sport Medicine)

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Review

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18 pages, 1288 KiB

Open AccessReview

Application of Artificial Intelligence in Shoulder Pathology

by Cong Cheng, Xinzhi Liang, Dong Guo and Denghui Xie

Diagnostics 2024, 14(11), 1091; https://doi.org/10.3390/diagnostics14111091 - 24 May 2024

Viewed by 1323

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. Full article

(This article belongs to the Special Issue Artificial Intelligence in Orthopedic Surgery and Sport Medicine)

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Other

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29 pages, 5732 KiB

Open AccessSystematic Review

A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis

by Alexei Botnari, Manuella Kadar and Jenel Marian Patrascu

Diagnostics 2024, 14(11), 1090; https://doi.org/10.3390/diagnostics14111090 - 24 May 2024

Viewed by 1045

Abstract

Objectives: This study delves into the cutting-edge field of deep learning techniques, particularly deep convolutional neural networks (DCNNs), which have demonstrated unprecedented potential in assisting radiologists and orthopedic surgeons in precisely identifying meniscal tears. This research aims to evaluate the effectiveness of deep learning models in recognizing, localizing, describing, and categorizing meniscal tears in magnetic resonance images (MRIs). Materials and methods: This systematic review was rigorously conducted, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Extensive searches were conducted on MEDLINE (PubMed), Web of Science, Cochrane Library, and Google Scholar. All identified articles underwent a comprehensive risk of bias analysis. Predictive performance values were either extracted or calculated for quantitative analysis, including sensitivity and specificity. The meta-analysis was performed for all prediction models that identified the presence and location of meniscus tears. Results: This study’s findings underscore that a range of deep learning models exhibit robust performance in detecting and classifying meniscal tears, in one case surpassing the expertise of musculoskeletal radiologists. Most studies in this review concentrated on identifying tears in the medial or lateral meniscus and even precisely locating tears—whether in the anterior or posterior horn—with exceptional accuracy, as demonstrated by AUC values ranging from 0.83 to 0.94. Conclusions: Based on these findings, deep learning models have showcased significant potential in analyzing knee MR images by learning intricate details within images. They offer precise outcomes across diverse tasks, including segmenting specific anatomical structures and identifying pathological regions. Contributions: This study focused exclusively on DL models for identifying and localizing meniscus tears. It presents a meta-analysis that includes eight studies for detecting the presence of a torn meniscus and a meta-analysis of three studies with low heterogeneity that localize and classify the menisci. Another novelty is the analysis of arthroscopic surgery as ground truth. The quality of the studies was assessed against the CLAIM checklist, and the risk of bias was determined using the QUADAS-2 tool. Full article

(This article belongs to the Special Issue Artificial Intelligence in Orthopedic Surgery and Sport Medicine)

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