1
|
Zampogna B, Torre G, Zampoli A, Parisi F, Ferrini A, Shanmugasundaram S, Franceschetti E, Papalia R. Can machine learning predict the accuracy of preoperative planning for total hip arthroplasty, basing on patient-related factors? An explorative investigation on Supervised machine learning classification models. J Clin Orthop Trauma 2024; 53:102470. [PMID: 39045495 PMCID: PMC11261062 DOI: 10.1016/j.jcot.2024.102470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 05/10/2024] [Accepted: 06/23/2024] [Indexed: 07/25/2024] Open
Abstract
Background The success of Total Hip Arthroplasty (THA) is influenced by preoperative planning, with traditional 2D approaches displaying varied reliability as well. The present study investigates the use of Supervised Machine Learning (SML) models with patient-related features to improve accuracy. Methods Preoperative and perioperative data, as well as planning and final implant information, were obtained from 800 consecutive cementless primary THA, which was performed uniformly by a specialized surgical team. Six Supervised Machine Learning models were trained and validated using patient characteristics and implant data: Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree (CART), Gaussian Naive Bayes (GN), and Support Vector Classifier (SVC). The models' ability to predict planning reliability and leg length disparity was evaluated. Results KNN performed better on the cup model (97.9 %), femur model (96.7 %), and femur size (99.2 %). SVM emerged as the model with the highest accuracy for cup size (60.4 %) and head size (62.1 %). CART had the best accuracy (99 %) when determining leg length discrepancy. Conclusion The study demonstrates the utility of Supervised Machine Learning models, specifically KNN, in predicting the accuracy of preoperative planning in THA. The accuracy of these models, which are driven by patient-related characteristics, provides useful information for optimizing patients' selection and improving surgical outcome.
Collapse
Affiliation(s)
- B. Zampogna
- Department of Orthopaedics and Trauma Surgery, Università Campus Bio-Medico di Roma, Roma, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
- BIOMORF Department, Biomedical, Dental and Morphological and Functional Images, Italy
- University of Messina. A.O.U. Policlinico “G.Martino” Messina, Italy
| | - G. Torre
- Villa Stuart Sport Clinic, FIFA Medical Centre of Excellence, Rome, Italy
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Rome, Italy
| | - A. Zampoli
- Department of Orthopaedics and Trauma Surgery, Università Campus Bio-Medico di Roma, Roma, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - F. Parisi
- Department of Orthopaedics and Trauma Surgery, Università Campus Bio-Medico di Roma, Roma, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - A. Ferrini
- Department of Clinical Science and Translational Medicine, Section of Orthopaedics and Traumatology, The University of Rome “Tor Vergata”, Rome, Italy
| | | | - E. Franceschetti
- Department of Orthopaedics and Trauma Surgery, Università Campus Bio-Medico di Roma, Roma, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - R. Papalia
- Department of Orthopaedics and Trauma Surgery, Università Campus Bio-Medico di Roma, Roma, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| |
Collapse
|
2
|
Tan JR, Gao Y, Raghuraman R, Ting D, Wong KM, Cheng LTE, Oh HC, Goh SH, Yan YY. Application of deep learning algorithms in classification and localization of implant cutout for the postoperative hip. Skeletal Radiol 2024:10.1007/s00256-024-04692-6. [PMID: 38771507 DOI: 10.1007/s00256-024-04692-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs. MATERIALS AND METHODS The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity. RESULTS The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions. CONCLUSION The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.
Collapse
Affiliation(s)
- Jin Rong Tan
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore General Hospital, Block 2, Level 1 Outram Road, Singapore, 169608, Singapore.
- Radiological Sciences ACP, Duke-NUS Medical School, Singapore, Singapore.
| | - Yan Gao
- Health Services Research, Changi General Hospital, Singapore Health Services, Singapore, Singapore
| | - Raghavan Raghuraman
- Department of Orthopaedic Surgery, Changi General Hospital, Singapore, Singapore
| | - Daniel Ting
- Duke-NUS Medical School, Singapore Health Service (SingHealth), Singapore, Singapore
| | - Kang Min Wong
- Radiological Sciences ACP, Duke-NUS Medical School, Singapore, Singapore
- Department of Radiology, Changi General Hospital, Singapore, Singapore
| | - Lionel Tim-Ee Cheng
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore General Hospital, Block 2, Level 1 Outram Road, Singapore, 169608, Singapore
- Radiological Sciences ACP, Duke-NUS Medical School, Singapore, Singapore
| | - Hong Choon Oh
- Health Services Research, Changi General Hospital, Singapore Health Services, Singapore, Singapore
| | - Siang Hiong Goh
- Department of Emergency Medicine, Changi General Hospital, Singapore, Singapore
| | - Yet Yen Yan
- Radiological Sciences ACP, Duke-NUS Medical School, Singapore, Singapore
- Department of Radiology, Changi General Hospital, Singapore, Singapore
| |
Collapse
|
3
|
Huffman N, Pasqualini I, Khan ST, Klika AK, Deren ME, Jin Y, Kunze KN, Piuzzi NS. Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review. JBJS Rev 2024; 12:01874474-202403000-00006. [PMID: 38466797 DOI: 10.2106/jbjs.rvw.23.00232] [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: 03/13/2024]
Abstract
» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.
Collapse
Affiliation(s)
- Nickelas Huffman
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | | | - Shujaa T Khan
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Alison K Klika
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Matthew E Deren
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Yuxuan Jin
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York
| | - Nicolas S Piuzzi
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
| |
Collapse
|
4
|
Gitto S, Serpi F, Albano D, Risoleo G, Fusco S, Messina C, Sconfienza LM. AI applications in musculoskeletal imaging: a narrative review. Eur Radiol Exp 2024; 8:22. [PMID: 38355767 PMCID: PMC10866817 DOI: 10.1186/s41747-024-00422-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/29/2023] [Indexed: 02/16/2024] Open
Abstract
This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice. Key points • AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance.
Collapse
Affiliation(s)
- Salvatore Gitto
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Francesca Serpi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milan, Italy
| | - Giovanni Risoleo
- Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, Milan, Italy
| | - Stefano Fusco
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
| | - Carmelo Messina
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Luca Maria Sconfienza
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy.
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
| |
Collapse
|
5
|
Tiwari A, Yadav AK, Akshay K, Bagaria V. Evaluation of machine learning models to identify hip arthroplasty implants using transfer learning algorithms. J Clin Orthop Trauma 2023; 47:102312. [PMID: 38196501 PMCID: PMC10772396 DOI: 10.1016/j.jcot.2023.102312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/06/2023] [Indexed: 01/11/2024] Open
Affiliation(s)
- Anjali Tiwari
- Department of Orthopedic Surgery, Sir H N Reliance Foundation Hospital, Girgaum, Mumbai, Maharashtra, India
| | - Amit Kumar Yadav
- International Training Fellow, Department of Trauma & Orthopedic Surgery, Wrightington Hospital, Wigan, UK
| | - K.S. Akshay
- Grant Government Medical College and Sir J J Group of Hospitals, India
| | - Vaibhav Bagaria
- Department of Orthopedic Surgery, Sir H N Reliance Foundation Hospital, Girgaum, Mumbai, Maharashtra, India
| |
Collapse
|
6
|
Kwolek K, Gądek A, Kwolek K, Kolecki R, Liszka H. Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographs. World J Orthop 2023; 14:800-812. [DOI: 10.5312/wjo.v14.i11.800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/11/2023] [Accepted: 10/30/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Assessment of the potential utility of deep learning with subsequent image analysis to automate the measurement of hallux valgus and intermetatarsal angles from radiographs to serve as a preoperative aid in establishing hallux valgus severity for clinical decision-making.
AIM To investigate the accuracy of automated measurements of angles of hallux valgus from radiographs for further integration with the preoperative planning process.
METHODS The data comprises 265 consecutive digital anteroposterior weightbearing foot radiographs. 181 radiographs were utilized for training (161) and validating (20) a U-Net neural network to achieve a mean Sørensen–Dice index > 97% on bone segmentation. 84 test radiographs were used for manual (computer assisted) and automated measurements of hallux valgus severity determined by hallux valgus (HVA) and intermetatarsal angles (IMA). The reliability of manual and computer-based measurements was calculated using the interclass correlation coefficient (ICC) and standard error of measurement (SEM). Inter- and intraobserver reliability coefficients were also compared. An operative treatment recommendation was then applied to compare results between automated and manual angle measurements.
RESULTS Very high reliability was achieved for HVA and IMA between the manual measurements of three independent clinicians. For HVA, the ICC between manual measurements was 0.96-0.99. For IMA, ICC was 0.78-0.95. Comparing manual against automated computer measurement, the reliability was high as well. For HVA, absolute agreement ICC and consistency ICC were 0.97, and SEM was 0.32. For IMA, absolute agreement ICC was 0.75, consistency ICC was 0.89, and SEM was 0.21. Additionally, a strong correlation (0.80) was observed between our approach and traditional clinical adjudication for preoperative planning of hallux valgus, according to an operative treatment algorithm proposed by EFORT.
CONCLUSION The proposed automated, artificial intelligence assisted determination of hallux valgus angles based on deep learning holds great potential as an accurate and efficient tool, with comparable accuracy to manual measurements by expert clinicians. Our approach can be effectively implemented in clinical practice to determine the angles of hallux valgus from radiographs, classify the deformity severity, streamline preoperative decision-making prior to corrective surgery.
Collapse
Affiliation(s)
- Konrad Kwolek
- Department of Orthopedics and Traumatology, University Hospital, Kraków 30-688, Małopolska, Poland
| | - Artur Gądek
- Department of Orthopedics and Physiotherapy, Jagiellonian University Collegium Medicum, Kraków 30-688, Małopolska, Poland
| | - Kamil Kwolek
- Department of Spine Disorders and Orthopedics, Gruca Orthopedic and Trauma Teaching Hospital, Otwock 05-400, Poland
| | - Radek Kolecki
- Department of Orthopedics and Traumatology, University Hospital, Kraków 30-688, Małopolska, Poland
| | - Henryk Liszka
- Department of Orthopedics and Physiotherapy, Jagiellonian University Collegium Medicum, Kraków 30-688, Małopolska, Poland
| |
Collapse
|
7
|
Guo S, Zhang J, Li H, Zhang J, Cheng CK. A multi-branch network to detect post-operative complications following hip arthroplasty on X-ray images. Front Bioeng Biotechnol 2023; 11:1239637. [PMID: 37840662 PMCID: PMC10569301 DOI: 10.3389/fbioe.2023.1239637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/13/2023] [Indexed: 10/17/2023] Open
Abstract
Background: Postoperative complications following total hip arthroplasty (THA) often require revision surgery. X-rays are usually used to detect such complications, but manually identifying the location of the problem and making an accurate assessment can be subjective and time-consuming. Therefore, in this study, we propose a multi-branch network to automatically detect postoperative complications on X-ray images. Methods: We developed a multi-branch network using ResNet as the backbone and two additional branches with a global feature stream and a channel feature stream for extracting features of interest. Additionally, inspired by our domain knowledge, we designed a multi-coefficient class-specific residual attention block to learn the correlations between different complications to improve the performance of the system. Results: Our proposed method achieved state-of-the-art (SOTA) performance in detecting multiple complications, with mean average precision (mAP) and F1 scores of 0.346 and 0.429, respectively. The network also showed excellent performance at identifying aseptic loosening, with recall and precision rates of 0.929 and 0.897, respectively. Ablation experiments were conducted on detecting multiple complications and single complications, as well as internal and external datasets, demonstrating the effectiveness of our proposed modules. Conclusion: Our deep learning method provides an accurate end-to-end solution for detecting postoperative complications following THA.
Collapse
Affiliation(s)
- Sijia Guo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Engineering Research Center for Digital Medicine of the Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Jiping Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Engineering Research Center for Digital Medicine of the Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Huiwu Li
- Department of Orthopaedics, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingwei Zhang
- Department of Orthopaedics, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Cheng-Kung Cheng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Engineering Research Center for Digital Medicine of the Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
8
|
Kurmis AP. A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty. ARTHROPLASTY 2023; 5:40. [PMID: 37400876 DOI: 10.1186/s42836-023-00189-0] [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: 02/03/2023] [Accepted: 04/19/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value. METHODS Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps. RESULTS A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as "demonstration of concepts" rather than "proof of concepts". There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited. CONCLUSION While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.
Collapse
Affiliation(s)
- Andrew P Kurmis
- Discipline of Medical Specialties, University of Adelaide, Adelaide, SA, 5005, Australia.
- Department of Orthopaedic Surgery, Lyell McEwin Hospital, Haydown Road, Elizabeth Vale, SA, 5112, Australia.
- College of Medicine & Public Health, Flinders University, Bedford Park, SA, 5042, Australia.
| |
Collapse
|
9
|
Kim MS, Cho RK, Yang SC, Hur JH, In Y. Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs. Bioengineering (Basel) 2023; 10:632. [PMID: 37370563 DOI: 10.3390/bioengineering10060632] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/15/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
(1) Background: The purpose of this study was to investigate whether the loosening of total knee arthroplasty (TKA) implants could be detected accurately on plain radiographs using a deep convolution neural network (CNN). (2) Methods: We analyzed data for 100 patients who underwent revision TKA due to prosthetic loosening at a single institution from 2012 to 2020. We extracted 100 patients who underwent primary TKA without loosening through a propensity score, matching for age, gender, body mass index, operation side, and American Society of Anesthesiologists class. Transfer learning was used to prepare a detection model using a pre-trained Visual Geometry Group (VGG) 19. For transfer learning, two methods were used. First, the fully connected layer was removed, and a new fully connected layer was added to construct a new model. The convolutional layer was frozen without training, and only the fully connected layer was trained (transfer learning model 1). Second, a new model was constructed by adding a fully connected layer and varying the range of freezing for the convolutional layer (transfer learning model 2). (3) Results: The transfer learning model 1 gradually increased in accuracy and ultimately reached 87.5%. After processing through the confusion matrix, the sensitivity was 90% and the specificity was 100%. Transfer learning model 2, which was trained on the convolutional layer, gradually increased in accuracy and ultimately reached 97.5%, which represented a better improvement than for model 1. Processing through the confusion matrix affirmed that the sensitivity was 100% and the specificity was 97.5%. (4) Conclusions: The CNN algorithm, through transfer learning, shows high accuracy for detecting the loosening of TKA implants on plain radiographs.
Collapse
Affiliation(s)
- Man-Soo Kim
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Ryu-Kyoung Cho
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Sung-Cheol Yang
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Jae-Hyeong Hur
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Yong In
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| |
Collapse
|
10
|
Muscato F, Corti A, Manlio Gambaro F, Chiappetta K, Loppini M, Corino VDA. Combining deep learning and machine learning for the automatic identification of hip prosthesis failure: Development, validation and explainability analysis. Int J Med Inform 2023; 176:105095. [PMID: 37220702 DOI: 10.1016/j.ijmedinf.2023.105095] [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: 03/13/2023] [Revised: 04/26/2023] [Accepted: 05/12/2023] [Indexed: 05/25/2023]
Abstract
AIM Revision hip arthroplasty has a less favorable outcome than primary total hip arthroplasty and an understanding of the timing of total hip arthroplasty failure may be helpful. The aim of this study is to develop a combined deep learning (DL) and machine learning (ML) approach to automatically detect hip prosthetic failure from conventional plain radiographs. METHODS Two cohorts of patients (of 280 and 352 patients) were included in the study, for model development and validation, respectively. The analysis was based on one antero-posterior and one lateral radiographic view obtained from each patient during routine post-surgery follow-up. After pre-processing, three images were obtained: the original image, the acetabulum image and the stem image. These images were analyzed through convolutional neural networks aiming to predict prosthesis failure. Deep features of the three images were extracted for each model and two feature-based pipelines were developed: one utilizing only the features of the original image (original image pipeline) and the other concatenating the features of the three images (3-image pipeline). The obtained features were either used directly or reduced through principal component analysis. Both support vector machine (SVM) and random forest (RF) classifiers were considered for each pipeline. RESULTS The SVM applied to the 3-image pipeline provided the best performance, with an accuracy of 0.958 ± 0.006 in the internal validation and an F1-score of 0.874 in the external validation set. The explainability analysis, besides identifying the features of the complete original images as the major contributor, highlighted the role of the acetabulum and stem images on the prediction. CONCLUSIONS This study demonstrated the potentialities of the developed DL-ML procedure based on plain radiographs in the detection of the failure of the hip prosthesis.
Collapse
Affiliation(s)
- Federico Muscato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Golgi 39, 20131 Milan, MI, Italy
| | - Anna Corti
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Francesco Manlio Gambaro
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, MI, Italy
| | - Katia Chiappetta
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, MI, Italy
| | - Mattia Loppini
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, MI, Italy; IRCCS Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, MI, Italy; Fondazione Livio Sciutto Ricerca Biomedica in Ortopedia-ONLUS, Via A. Magliotto 2, 17100 Savona, SV, Italy
| | - Valentina D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Golgi 39, 20131 Milan, MI, Italy; Cardio Tech-Lab, Centro Cardiologico Monzino IRCCS, Via Carlo Parea 4, 20138 Milan, Italy.
| |
Collapse
|
11
|
Kim MS, Kim JJ, Kang KH, Lee JH, In Y. Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040782. [PMID: 37109740 PMCID: PMC10141023 DOI: 10.3390/medicina59040782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 04/02/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023]
Abstract
Background: prosthetic loosening after hip and knee arthroplasty is one of the most common causes of joint arthroplasty failure and revision surgery. Diagnosis of prosthetic loosening is a difficult problem and, in many cases, loosening is not clearly diagnosed until accurately confirmed during surgery. The purpose of this study is to conduct a systematic review and meta-analysis to demonstrate the analysis and performance of machine learning in diagnosing prosthetic loosening after total hip arthroplasty (THA) and total knee arthroplasty (TKA). Materials and Methods: three comprehensive databases, including MEDLINE, EMBASE, and the Cochrane Library, were searched for studies that evaluated the detection accuracy of loosening around arthroplasty implants using machine learning. Data extraction, risk of bias assessment, and meta-analysis were performed. Results: five studies were included in the meta-analysis. All studies were retrospective studies. In total, data from 2013 patients with 3236 images were assessed; these data involved 2442 cases (75.5%) with THAs and 794 cases (24.5%) with TKAs. The most common and best-performing machine learning algorithm was DenseNet. In one study, a novel stacking approach using a random forest showed similar performance to DenseNet. The pooled sensitivity across studies was 0.92 (95% CI 0.84-0.97), the pooled specificity was 0.95 (95% CI 0.93-0.96), and the pooled diagnostic odds ratio was 194.09 (95% CI 61.60-611.57). The I2 statistics for sensitivity and specificity were 96% and 62%, respectively, showing that there was significant heterogeneity. The summary receiver operating characteristics curve indicated the sensitivity and specificity, as did the prediction regions, with an AUC of 0.9853. Conclusions: the performance of machine learning using plain radiography showed promising results with good accuracy, sensitivity, and specificity in the detection of loosening around THAs and TKAs. Machine learning can be incorporated into prosthetic loosening screening programs.
Collapse
Affiliation(s)
- Man-Soo Kim
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Jae-Jung Kim
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Ki-Ho Kang
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Jeong-Han Lee
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Yong In
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| |
Collapse
|
12
|
Albano D, Gitto S, Messina C, Serpi F, Salvatore C, Castiglioni I, Zagra L, De Vecchi E, Sconfienza LM. MRI-based artificial intelligence to predict infection following total hip arthroplasty failure. LA RADIOLOGIA MEDICA 2023; 128:340-346. [PMID: 36786971 PMCID: PMC10020270 DOI: 10.1007/s11547-023-01608-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/03/2023] [Indexed: 02/15/2023]
Abstract
PURPOSE To investigate whether artificial intelligence (AI) can differentiate septic from non-septic total hip arthroplasty (THA) failure based on preoperative MRI features. MATERIALS AND METHODS We included 173 patients (98 females, age: 67 ± 12 years) subjected to first-time THA revision surgery after preoperative pelvis MRI. We divided the patients into a training/validation/internal testing cohort (n = 117) and a temporally independent external-testing cohort (n = 56). MRI features were used to train, validate and test a machine learning algorithm based on support vector machine (SVM) to predict THA infection on the training-internal validation cohort with a nested fivefold validation approach. Machine learning performance was evaluated on independent data from the external-testing cohort. RESULTS MRI features were significantly more frequently observed in THA infection (P < 0.001), except bone destruction, periarticular soft-tissue mass, and fibrous membrane (P > 0.005). Considering all MRI features in the training/validation/internal-testing cohort, SVM classifier reached 92% sensitivity, 62% specificity, 79% PPV, 83% NPV, 82% accuracy, and 81% AUC in predicting THA infection, with bone edema, extracapsular edema, and synovitis having been the best predictors. After being tested on the external-testing cohort, the classifier showed 92% sensitivity, 79% specificity, 89% PPV, 83% NPV, 88% accuracy, and 89% AUC in predicting THA infection. SVM classifier showed 81% sensitivity, 76% specificity, 66% PPV, 88% NPV, 80% accuracy, and 74% AUC in predicting THA infection in the training/validation/internal-testing cohort based on the only presence of periprosthetic bone marrow edema on MRI, while it showed 68% sensitivity, 89% specificity, 93% PPV, 60% NPV, 75% accuracy, and 79% AUC in the external-testing cohort. CONCLUSION AI using SVM classifier showed promising results in predicting THA infection based on MRI features. This model might support radiologists in identifying THA infection.
Collapse
Affiliation(s)
- Domenico Albano
- Unità Operativa Di Radiologia Diagnostica E Interventistica, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy.
| | - Salvatore Gitto
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, 20133, Milan, Italy
| | - Carmelo Messina
- Unità Operativa Di Radiologia Diagnostica E Interventistica, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, 20133, Milan, Italy
| | - Francesca Serpi
- Unità Operativa Di Radiologia Diagnostica E Interventistica, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, 20133, Milan, Italy
| | - Christian Salvatore
- DeepTrace Technologies S.R.L., Milan, Italy
- Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, Pavia, Italy
| | - Isabella Castiglioni
- Department of Physics, Università Degli Studi Di Milano-Bicocca, 20126, Milan, Italy
- Institute of Biomedical Imaging and Physiology, Consiglio Nazionale Delle Ricerche, 20090, Segrate, Italy
| | - Luigi Zagra
- Hip Department, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy
| | - Elena De Vecchi
- Laboratory of Clinical Chemistry and Microbiology, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy
| | - Luca Maria Sconfienza
- Unità Operativa Di Radiologia Diagnostica E Interventistica, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, 20133, Milan, Italy
| |
Collapse
|
13
|
Oeding JF, Williams RJ, Nwachukwu BU, Martin RK, Kelly BT, Karlsson J, Camp CL, Pearle AD, Ranawat AS, Pareek A. A practical guide to the development and deployment of deep learning models for the Orthopedic surgeon: part I. Knee Surg Sports Traumatol Arthrosc 2023; 31:382-389. [PMID: 36427077 DOI: 10.1007/s00167-022-07239-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 11/15/2022] [Indexed: 11/26/2022]
Abstract
Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning-based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre-processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided. This review strives to provide surgeons with the groundwork needed to identify gaps in the clinical landscape that deep learning models may be able to fill and equips them with the knowledge needed to lead an interdisciplinary team through the process of creating novel deep-learning-based solutions to fill those gaps.
Collapse
Affiliation(s)
- Jacob F Oeding
- School of Medicine, Mayo Clinic Alix School of Medicine, Rochester, MN, USA
| | - Riley J Williams
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Benedict U Nwachukwu
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - R Kyle Martin
- Department of Orthopedic Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Bryan T Kelly
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Jón Karlsson
- Department of Orthopaedics, Sahlgrenska University Hospital, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - Christopher L Camp
- Department of Orthopedic Surgery and Sports Medicine, Rochester, MN, USA
| | - Andrew D Pearle
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Anil S Ranawat
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Ayoosh Pareek
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
| |
Collapse
|
14
|
Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study. Med Biol Eng Comput 2023; 61:1239-1255. [PMID: 36701013 DOI: 10.1007/s11517-023-02779-1] [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: 08/24/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023]
Abstract
The digital health industry is experiencing fast-paced research which can provide digital care programs and technologies to enhance the competence of healthcare delivery. Orthopedic literature also confirms the applicability of artificial intelligence (AI) and machine learning (ML) models to medical diagnosis and clinical decision-making. However, implant monitoring after primary surgery often happens with a wellness visit or when a patient complains about it. Neglecting implant design and other technical errors in this scenario, unmonitored circumstances, and lack of post-surgery monitoring may ultimately lead to the implant system's failure and leave us with the only option of high-risk revision surgery. Preventive maintenance seems to be a good choice to identify the onset of an irreversible prosthesis failure. Considering all these aspects for hip implant monitoring, this paper explores existing studies linking ML models and intelligent systems for hip implant diagnosis. This paper explores the feasibility of an alternative continuous monitoring technique for post-surgery implant monitoring backed by an in vitro ML case study. Tribocorrosion and acoustic emission (AE) data are considered based on their efficacy in determining irreversible alteration of implant material to prevent total failures. This study also facilitates the relevance of developing an artificially intelligent implant monitoring methodology that can function with daily patient activities and how it can influence the digital orthopedic diagnosis. AI-based non-invasive hip implant monitoring system enabling point-of-care testing.
Collapse
|
15
|
Haddad FS. Looking back over the past year. Bone Joint J 2022; 104-B:1279-1280. [DOI: 10.1302/0301-620x.104b12.bjj-2022-1161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Affiliation(s)
- Fares S. Haddad
- University College London Hospitals, The Princess Grace Hospital, and The NIHR Biomedical Research Centre at UCLH, London, UK
- The Bone & Joint Journal, London, UK
| |
Collapse
|
16
|
Polisetty TS, Jain S, Pang M, Karnuta JM, Vigdorchik JM, Nawabi DH, Wyles CC, Ramkumar PN. Concerns surrounding application of artificial intelligence in hip and knee arthroplasty. Bone Joint J 2022; 104-B:1292-1303. [DOI: 10.1302/0301-620x.104b12.bjj-2022-0922.r1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article: Bone Joint J 2022;104-B(12):1292–1303.
Collapse
Affiliation(s)
- Teja S. Polisetty
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Samagra Jain
- Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Michael Pang
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jaret M. Karnuta
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Danyal H. Nawabi
- Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA
| | - Cody C. Wyles
- Department of Orthopaedic Surgery, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Prem N. Ramkumar
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA
| |
Collapse
|
17
|
A novel image-based machine learning model with superior accuracy and predictability for knee arthroplasty loosening detection and clinical decision making. J Orthop Translat 2022; 36:177-183. [PMID: 36263380 PMCID: PMC9562957 DOI: 10.1016/j.jot.2022.07.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/13/2022] [Accepted: 07/04/2022] [Indexed: 11/08/2022] Open
Abstract
Background Loosening is the leading cause of total knee arthroplasty (TKA) revision. This is a heavy burden toward the healthcare system owing to the difficulty in diagnosis and complications occurring from the delay management. Based on automatic analytical model building, machine learning, may potentially help to automatically recognize the risk of loosening based on radiographs alone. The aim of this study was to build an image-based machine-learning model for detecting TKA loosening. Methods Image-based machine-learning model was developed based on ImageNet, Xception model and a TKA patient X-ray image dataset. Based on a dataset with TKA patient clinical parameters, another system was then created for developing the clinical-information-based machine learning model with random forest classifier. In addition, the Xception Model was pre-trained on the ImageNet database with python and TensorFlow deep learning library for the prediction of loosening. Class activation maps were also used to interpret the prediction decision made by model. Two senior orthopaedic specialists were invited to assess loosening from X-ray images for 3 attempts in setting up comparison benchmark. Result In the image-based machine learning loosening model, the precision rate and recall rate were 0.92 and 0.96, respectively. While for the accuracy rate, 96.3% for visualization classification was observed. However, the addition of clinical-information-based model, with precision rate of 0.71 and recall rate of 0.20, did not further showed improvement on the accuracy. Moreover, as class activation maps showed corresponding signals over bone-implant interface that is loosened radiographically, this confirms that the current model utilized a similar image recognition pattern as that of inspection by clinical specialists. Conclusion The image-based machine learning model developed demonstrated high accuracy and predictability of knee arthroplasty loosening. And the class activation heatmap matched well with the radiographic features used clinically to detect loosening, which highlighting its potential role in assisting clinicians in their daily practice. However, addition of clinical-information-based machine-learning model did not offer further improvement in detection. As far as we know, this is the first report of pure image-based machine learning model with high detection accuracy. Importantly, this is also the first model to show relevant class activation heatmap corresponding to loosening location. Translational potential The finding in this study indicated image-based machine learning model can detect knee arthroplasty loosening with high accuracy and predictability, which the class activation heatmap can potentially assist surgeons to identify the sites of loosening.
Collapse
|
18
|
Hill BG, Krogue JD, Jevsevar DS, Schilling PL. Deep Learning and Imaging for the Orthopaedic Surgeon: How Machines "Read" Radiographs. J Bone Joint Surg Am 2022; 104:1675-1686. [PMID: 35867718 DOI: 10.2106/jbjs.21.01387] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
➤ In the not-so-distant future, orthopaedic surgeons will be exposed to machines that begin to automatically "read" medical imaging studies using a technology called deep learning. ➤ Deep learning has demonstrated remarkable progress in the analysis of medical imaging across a range of modalities that are commonly used in orthopaedics, including radiographs, computed tomographic scans, and magnetic resonance imaging scans. ➤ There is a growing body of evidence showing clinical utility for deep learning in musculoskeletal radiography, as evidenced by studies that use deep learning to achieve an expert or near-expert level of performance for the identification and localization of fractures on radiographs. ➤ Deep learning is currently in the very early stages of entering the clinical setting, involving validation and proof-of-concept studies for automated medical image interpretation. ➤ The success of deep learning in the analysis of medical imaging has been propelling the field forward so rapidly that now is the time for surgeons to pause and understand how this technology works at a conceptual level, before (not after) the technology ends up in front of us and our patients. That is the purpose of this article.
Collapse
Affiliation(s)
- Brandon G Hill
- Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire
| | - Justin D Krogue
- Google Health, Palo Alto, California.,Department of Orthopaedic Surgery, University of California San Francisco, San Francisco, California
| | - David S Jevsevar
- Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire.,The Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Peter L Schilling
- Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire.,The Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| |
Collapse
|
19
|
Vail TP. Deep Learning Dramatically Reduces the Work Associated with Image Cataloguing and Analysis: Commentary on an article by Pouria Rouzrokh, MD, MPH, MHPE, et al.: "Applying Deep Learning to Establish a Total Hip Arthroplasty Radiography Registry. A Stepwise Approach". J Bone Joint Surg Am 2022; 104:e82. [PMID: 36129678 DOI: 10.2106/jbjs.22.00664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Thomas Parker Vail
- Department of Orthopaedic Surgery, University of California San Francisco, San Francisco, California
| |
Collapse
|
20
|
Gurung B, Liu P, Harris PDR, Sagi A, Field RE, Sochart DH, Tucker K, Asopa V. Artificial intelligence for image analysis in total hip and total knee arthroplasty : a scoping review. Bone Joint J 2022; 104-B:929-937. [PMID: 35909383 DOI: 10.1302/0301-620x.104b8.bjj-2022-0120.r2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
AIMS Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. METHODS The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O'Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy. RESULTS Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. CONCLUSION These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929-937.
Collapse
Affiliation(s)
- Binay Gurung
- South West London Elective Orthopaedic Centre, Epsom, UK
| | - Perry Liu
- South West London Elective Orthopaedic Centre, Epsom, UK
| | | | - Amit Sagi
- South West London Elective Orthopaedic Centre, Epsom, UK.,Barzilai Medical Centre, Ashkelon, Israel
| | - Richard E Field
- South West London Elective Orthopaedic Centre, Epsom, UK.,St George's, University of London, London, UK
| | | | - Keith Tucker
- South West London Elective Orthopaedic Centre, Epsom, UK.,Orthopaedics Data Evaluation Panel, London, UK
| | - Vipin Asopa
- South West London Elective Orthopaedic Centre, Epsom, UK
| |
Collapse
|
21
|
Automatic Identification of Failure in Hip Replacement: An Artificial Intelligence Approach. Bioengineering (Basel) 2022; 9:bioengineering9070288. [PMID: 35877339 PMCID: PMC9312125 DOI: 10.3390/bioengineering9070288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/17/2022] [Accepted: 06/26/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Total hip arthroplasty (THA) follow-up is conventionally conducted with serial X-ray imaging in order to ensure the early identification of implant failure. The purpose of this study is to develop an automated radiographic failure detection system. Methods: 630 patients with THA were included in the study, two thirds of which needed total or partial revision for prosthetic loosening. The analysis is based on one antero-posterior and one lateral radiographic view obtained from each patient during routine post-surgery follow-up. After pre-processing for proper standardization, images were analyzed through a convolutional neural network (the DenseNet169 network), aiming to predict prosthesis failure. The entire dataset was divided in three subsets: training, validation, and test. These contained transfer learning and fine-tuning algorithms, based on the training dataset, and were implemented to adapt the DenseNet169 network to the specific data and clinical problem. Results: After the training procedures, in the test set, the classification accuracy was 0.97, the sensitivity 0.97, the specificity 0.97, and the ROC AUC was 0.99. Only five images were incorrectly classified. Seventy-four images were classified as failed, and eighty as non-failed with a probability >0.999. Conclusion: The proposed deep learning procedure can detect the loosening of the hip prosthesis with a very high degree of precision.
Collapse
|
22
|
Polce EM, Kunze KN, Dooley MS, Piuzzi NS, Boettner F, Sculco PK. Efficacy and Applications of Artificial Intelligence and Machine Learning Analyses in Total Joint Arthroplasty: A Call for Improved Reporting. J Bone Joint Surg Am 2022; 104:821-832. [PMID: 35045061 DOI: 10.2106/jbjs.21.00717] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND There has been a considerable increase in total joint arthroplasty (TJA) research using machine learning (ML). Therefore, the purposes of this study were to synthesize the applications and efficacies of ML reported in the TJA literature, and to assess the methodological quality of these studies. METHODS PubMed, OVID/MEDLINE, and Cochrane libraries were queried in January 2021 for articles regarding the use of ML in TJA. Study demographics, topic, primary and secondary outcomes, ML model development and testing, and model presentation and validation were recorded. The TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were used to assess the methodological quality. RESULTS Fifty-five studies were identified: 31 investigated clinical outcomes and resource utilization; 11, activity and motion surveillance; 10, imaging detection; and 3, natural language processing. For studies reporting the area under the receiver operating characteristic curve (AUC), the median AUC (and range) was 0.80 (0.60 to 0.97) among 26 clinical outcome studies, 0.99 (0.83 to 1.00) among 6 imaging-based studies, and 0.88 (0.76 to 0.98) among 3 activity and motion surveillance studies. Twelve studies compared ML to logistic regression, with 9 (75%) reporting that ML was superior. The average number of TRIPOD guidelines met was 11.5 (range: 5 to 18), with 38 (69%) meeting greater than half of the criteria. Presentation and explanation of the full model for individual predictions and assessments of model calibration were poorly reported (<30%). CONCLUSIONS The performance of ML models was good to excellent when applied to a wide variety of clinically relevant outcomes in TJA. However, reporting of certain key methodological and model presentation criteria was inadequate. Despite the recent surge in TJA literature utilizing ML, the lack of consistent adherence to reporting guidelines needs to be addressed to bridge the gap between model development and clinical implementation.
Collapse
Affiliation(s)
- Evan M Polce
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Matthew S Dooley
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Nicolas S Piuzzi
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio
| | - Friedrich Boettner
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Peter K Sculco
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| |
Collapse
|
23
|
Batailler C, Shatrov J, Sappey-Marinier E, Servien E, Parratte S, Lustig S. Artificial intelligence in knee arthroplasty: current concept of the available clinical applications. ARTHROPLASTY 2022; 4:17. [PMID: 35491420 PMCID: PMC9059406 DOI: 10.1186/s42836-022-00119-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 02/24/2022] [Indexed: 11/30/2022] Open
Abstract
Background Artificial intelligence (AI) is defined as the study of algorithms that allow machines to reason and perform cognitive functions such as problem-solving, objects, images, word recognition, and decision-making. This study aimed to review the published articles and the comprehensive clinical relevance of AI-based tools used before, during, and after knee arthroplasty. Methods The search was conducted through PubMed, EMBASE, and MEDLINE databases from 2000 to 2021 using the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA). Results A total of 731 potential articles were reviewed, and 132 were included based on the inclusion criteria and exclusion criteria. Some steps of the knee arthroplasty procedure were assisted and improved by using AI-based tools. Before surgery, machine learning was used to aid surgeons in optimizing decision-making. During surgery, the robotic-assisted systems improved the accuracy of knee alignment, implant positioning, and ligamentous balance. After surgery, remote patient monitoring platforms helped to capture patients’ functional data. Conclusion In knee arthroplasty, the AI-based tools improve the decision-making process, surgical planning, accuracy, and repeatability of surgical procedures.
Collapse
|
24
|
Application of deep learning algorithm in automated identification of knee arthroplasty implants from plain radiographs using transfer learning models: Are algorithms better than humans? J Orthop 2022; 32:139-145. [DOI: 10.1016/j.jor.2022.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 01/16/2023] Open
|
25
|
Kurmis AP, Ianunzio JR. Artificial intelligence in orthopedic surgery: evolution, current state and future directions. ARTHROPLASTY 2022; 4:9. [PMID: 35232490 PMCID: PMC8889658 DOI: 10.1186/s42836-022-00112-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/31/2021] [Indexed: 12/14/2022] Open
Abstract
Technological advances continue to evolve at a breath-taking pace. Computer-navigation, robot-assistance and three-dimensional digital planning have become commonplace in many parts of the world. With near exponential advances in computer processing capacity, and the advent, progressive understanding and refinement of software algorithms, medicine and orthopaedic surgery have begun to delve into artificial intelligence (AI) systems. While for some, such applications still seem in the realm of science fiction, these technologies are already in selective clinical use and are likely to soon see wider uptake. The purpose of this structured review was to provide an understandable summary to non-academic orthopaedic surgeons, exploring key definitions and basic development principles of AI technology as it currently stands. To ensure content validity and representativeness, a structured, systematic review was performed following the accepted PRISMA principles. The paper concludes with a forward-look into heralded and potential applications of AI technology in orthopedic surgery.While not intended to be a detailed technical description of the complex processing that underpins AI applications, this work will take a small step forward in demystifying some of the commonly-held misconceptions regarding AI and its potential benefits to patients and surgeons. With evidence-supported broader awareness, we aim to foster an open-mindedness among clinicians toward such technologies in the future.
Collapse
Affiliation(s)
- Andrew P Kurmis
- Discipline of Medical Specialties, University of Adelaide, Adelaide, SA, Australia. .,Department of Orthopaedic Surgery, Lyell McEwin Hospital, Vale, Elizabeth, SA, Australia.
| | - Jamie R Ianunzio
- Department of Orthopaedic Surgery, Lyell McEwin Hospital, Vale, Elizabeth, SA, Australia.,School of Medicine, University of Adelaide, Adelaide, SA, Australia
| |
Collapse
|
26
|
Wang Z, Wei S. Local treatment with Sema3a could promote the osseointegration of hydroxyapatite coated titanium rod in diabetic rats. J Biomater Appl 2022; 36:1775-1785. [PMID: 35225049 DOI: 10.1177/08853282221075707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Recently, semaphorin 3A (Sema3A) has been identified as a critical gene for osteogenic differentiation of mesenchymal stem cells and increases osteoblastic bone formation. However, in current research studies, there is a lack of focus on whether Sema3a can affect the osseointegration of titanium rods in diabetes and through what biological mechanisms. Therefore, the present work was aimed to evaluate the effect of local administration with Sema3A on hydroxyapatite coated titanium rod osseointegration in diabetic rat model and preliminary exploration of possible mechanisms. The MC3T3-E1 cells were co-cultured with Sema3A and high glucose and induced to osteogenesis, and the cell viability, osteogenic activity was observed by Cell Counting Kit-8(CCK-8), Alkaline Phosphatase staining, Alizarin Red Staining, and Western Blot. In vitro experiments, CCK-8, ALP, and ARS staining results show that the mineralization and osteogenic activity of MC3T3-E1increased significantly after intervention by Sema3A, as well as a higher levels of protein expressions including Runt-Related Transcription Factor 2, silent mating type information regulation 2 homolog-1(SIRT1), catalase (CAT), superoxide dismutase 1 (SOD1), and superoxide dismutase 2 (SOD2). In vivo experiment, a better stability and osseointegration of the titanium rod were observed after treatment with Sema3A, as well as a higher SOD1, SOD2, CAT, and SIRT1 gene expression. The present study indicates that local treatment with Sema3A was associated with increased osseointegration of titanium rod by reducing the oxidative stress of osteoblasts and enhancing the function of osteoblasts in a diabetic rat.
Collapse
Affiliation(s)
- Zhengyu Wang
- Department of Orthopedics, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, No. 2, Zhe Shan Xi Road, Wuhu 241001, Anhui, People's Republic of China
| | - Shan Wei
- School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, 241000, P.R. China
- Additive Manufacturing Institute of Anhui Polytechnic University, Anhui Polytechnic University, Wuhu, 241000, P.R. China
| |
Collapse
|
27
|
Applications of artificial intelligence and machine learning for the hip and knee surgeon: current state and implications for the future. INTERNATIONAL ORTHOPAEDICS 2022; 46:937-944. [PMID: 35171335 DOI: 10.1007/s00264-022-05346-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/05/2022] [Indexed: 12/17/2022]
Abstract
BACKGROUND Artificial Intelligence (AI)/Machine Learning (ML) applications have been proven efficient to improve diagnosis, to stratify risk, and to predict outcomes in many respective medical specialties, including in orthopaedics. CHALLENGES AND DISCUSSION Regarding hip and knee reconstruction surgery, AI/ML have not made it yet to clinical practice. In this review, we present sound AI/ML applications in the field of hip and knee degenerative disease and reconstruction. From osteoarthritis (OA) diagnosis and prediction of its advancement, clinical decision-making, identification of hip and knee implants to prediction of clinical outcome and complications following a reconstruction procedure of these joints, we report how AI/ML systems could facilitate data-driven personalized care for our patients.
Collapse
|
28
|
Calivà F, Namiri NK, Dubreuil M, Pedoia V, Ozhinsky E, Majumdar S. Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging. Nat Rev Rheumatol 2022; 18:112-121. [PMID: 34848883 DOI: 10.1038/s41584-021-00719-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2021] [Indexed: 02/08/2023]
Abstract
The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis research, by facilitating the elucidation of disease pathogenesis and progression. The recent increasing employment of MRI has certainly been stimulated by major advances that are due to considerable investment in research, particularly related to artificial intelligence (AI). These AI-related advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times of MRI, conducting large-scale longitudinal studies and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field to improve understanding of osteoarthritis. Compared with labour-intensive human efforts, AI-based methods have advantages and potential in all stages of imaging, as well as post-processing steps, including aiding diagnosis and prognosis. However, AI-based methods also have limitations, including the arguably limited interpretability of AI models. Given that the AI community is highly invested in uncovering uncertainties associated with model predictions and improving their interpretability, we envision future clinical translation and progressive increase in the use of AI algorithms to support clinicians in optimizing patient care.
Collapse
Affiliation(s)
- Francesco Calivà
- Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Nikan K Namiri
- Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Maureen Dubreuil
- Section of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Eugene Ozhinsky
- Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, USA.
| |
Collapse
|
29
|
AI MSK clinical applications: orthopedic implants. Skeletal Radiol 2022; 51:305-313. [PMID: 34350476 DOI: 10.1007/s00256-021-03879-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/15/2021] [Accepted: 07/22/2021] [Indexed: 02/02/2023]
Abstract
Artificial intelligence (AI) and deep learning have multiple potential uses in aiding the musculoskeletal radiologist in the radiological evaluation of orthopedic implants. These include identification of implants, characterization of implants according to anatomic type, identification of specific implant models, and evaluation of implants for positioning and complications. In addition, natural language processing (NLP) can aid in the acquisition of clinical information from the medical record that can help with tasks like prepopulating radiology reports. Several proof-of-concept works have been published in the literature describing the application of deep learning toward these various tasks, with performance comparable to that of expert musculoskeletal radiologists. Although much work remains to bring these proof-of-concept algorithms into clinical deployment, AI has tremendous potential toward automating these tasks, thereby augmenting the musculoskeletal radiologist.
Collapse
|
30
|
Kunze KN, Orr M, Krebs V, Bhandari M, Piuzzi NS. Potential benefits, unintended consequences, and future roles of artificial intelligence in orthopaedic surgery research : a call to emphasize data quality and indications. Bone Jt Open 2022; 3:93-97. [PMID: 35084227 PMCID: PMC9047073 DOI: 10.1302/2633-1462.31.bjo-2021-0123.r1] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
Collapse
Affiliation(s)
- Kyle N Kunze
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Melissa Orr
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA
| | - Viktor Krebs
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA
| | - Mohit Bhandari
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.,Department of Surgery, Division of Orthopaedic Surgery, McMaster University, Cleveland, Ohio, USA
| | - Nicolas S Piuzzi
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA
| |
Collapse
|
31
|
Purnomo G, Yeo SJ, Liow MHL. Artificial intelligence in arthroplasty. ARTHROPLASTY 2021; 3:37. [PMID: 35236494 PMCID: PMC8796516 DOI: 10.1186/s42836-021-00095-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/31/2021] [Indexed: 01/10/2023] Open
Abstract
Artificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.
Collapse
Affiliation(s)
- Glen Purnomo
- St. Vincentius a Paulo Catholic Hospital, Surabaya, Indonesia.
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore.
| | - Seng-Jin Yeo
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Ming Han Lincoln Liow
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| |
Collapse
|
32
|
Sun T, Xing HL, Chen ZZ, Tao ZS, Li J. Simvastatin reverses the harmful effects of high fat diet on titanium rod osseointegration in ovariectomized rats. J Bone Miner Metab 2021; 39:944-951. [PMID: 34189660 DOI: 10.1007/s00774-021-01243-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 05/31/2021] [Indexed: 11/30/2022]
Abstract
INTRODUCTION The objectives of the present study were to determine whether simvastatin (SIM) could reverse the harmful effects on titanium rod osseointegration in ovariectomized rats fed high-fat diet (HFD). MATERIALS AND METHODS Ovariectomized female Sprague-Dawley rats were randomly allocated to three groups and received SIM treatment plus HFD for 12 weeks. We then evaluated the microstructure parameters, histological parameters, biomechanical parameters, bone turnover, and blood lipid level. RESULTS After 12 weeks of treatment, SIM can significantly improve bone formation around the titanium rod and osseointegration including higher values of maximum push-out force, bone area ratio (BAR), bone-to-implant contact (BIC), bone mineral density (BMD), bone volume (BV/TV), trabecular number (Tb.N), trabecular thickness (Tb.Th), mean connective density (Conn.D) when compared with the HFD group. In addition, system administration of SIM showed positive effects on collagen type 1 cross-linked C-telopeptide (CTX-1), procollagen I N-terminal propeptide (PINP), total cholesterol (TC), triglycerides (TGL), low-density lipoprotein (LDL) cholesterol and high-density lipoprotein (HDL) cholesterol. Compared with the HFD group, lower values of CTX-1, P1NP, TC, TGL and LDL were observed in the SIM+HFD group (P < 0.05). CONCLUSION Our findings revealed that HFD may have an adverse effect on osseointegration in osteoporotic conditions, and the harmful effect of HFD on osseointegration could be reversed by SIM.
Collapse
Affiliation(s)
- Tao Sun
- Department of Orthopedics, The Fifth Affiliated Hospital Lishui Municipal Central Hospital, Wenzhou Medical University, Lishui, Zhejiang, People's Republic of China
| | - Hai-Lin Xing
- Department of Orthopedics, The Fifth Affiliated Hospital Lishui Municipal Central Hospital, Wenzhou Medical University, Lishui, Zhejiang, People's Republic of China
| | - Zhen-Zhong Chen
- Department of Orthopedics, The Fifth Affiliated Hospital Lishui Municipal Central Hospital, Wenzhou Medical University, Lishui, Zhejiang, People's Republic of China
| | - Zhou-Shan Tao
- Department of Trauma Orthopedics, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, Anhui, People's Republic of China
| | - Jian Li
- Department of Orthopedics, The Fifth Affiliated Hospital Lishui Municipal Central Hospital, Wenzhou Medical University, Lishui, Zhejiang, People's Republic of China.
| |
Collapse
|
33
|
Wei C, Quan T, Wang KY, Gu A, Fassihi SC, Kahlenberg CA, Malahias MA, Liu J, Thakkar S, Gonzalez Della Valle A, Sculco PK. Artificial neural network prediction of same-day discharge following primary total knee arthroplasty based on preoperative and intraoperative variables. Bone Joint J 2021; 103-B:1358-1366. [PMID: 34334050 DOI: 10.1302/0301-620x.103b8.bjj-2020-1013.r2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AIMS This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). METHODS Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay. RESULTS The predictability of the ANN model, area under the curve (AUC) = 0.801, was similar to the logistic regression model (AUC = 0.796) and identified certain variables as important factors to predict same-day discharge. The ten most important factors favouring same-day discharge in the ANN model include preoperative sodium, preoperative international normalized ratio, BMI, age, anaesthesia type, operating time, dyspnoea status, functional status, race, anaemia status, and chronic obstructive pulmonary disease (COPD). Six of these variables were also found to be significant on logistic regression analysis. CONCLUSION Both ANN modelling and logistic regression analysis revealed clinically important factors in predicting patients who can undergo safely undergo same-day discharge from an outpatient TKA. The ANN model provides a beneficial approach to help determine which perioperative factors can predict same-day discharge as of 2018 perioperative recovery protocols. Cite this article: Bone Joint J 2021;103-B(8):1358-1366.
Collapse
Affiliation(s)
- Chapman Wei
- Department of Orthopaedic Surgery, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Theodore Quan
- Department of Orthopaedic Surgery, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Kevin Y Wang
- Johns Hopkins Department of Orthopaedic Surgery, Adult Reconstruction Division, John Hopkins Medicine, Baltimore, Maryland, USA
| | - Alex Gu
- Department of Orthopaedic Surgery, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA.,The Stavros Niarchos Foundation Complex Joint Reconstruction Center, Department of Orthopaedic Surgery, Hospital for Special Surgery, Washington, District of Columbia, USA
| | - Safa C Fassihi
- Department of Orthopaedic Surgery, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Cynthia A Kahlenberg
- Adult Reconstruction and Joint Replacement Division, Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Michael-Alexander Malahias
- Adult Reconstruction and Joint Replacement Division, Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Jiabin Liu
- Department of Anesthesiology, Critical Care & Pain Management, Hospital for Special Surgery, New York, New York, USA
| | - Savyasachi Thakkar
- Johns Hopkins Department of Orthopaedic Surgery, Adult Reconstruction Division, John Hopkins Medicine, Baltimore, Maryland, USA
| | - Alejandro Gonzalez Della Valle
- The Stavros Niarchos Foundation Complex Joint Reconstruction Center, Department of Orthopaedic Surgery, Hospital for Special Surgery, Washington, District of Columbia, USA
| | - Peter K Sculco
- The Stavros Niarchos Foundation Complex Joint Reconstruction Center, Department of Orthopaedic Surgery, Hospital for Special Surgery, Washington, District of Columbia, USA
| |
Collapse
|
34
|
Tao ZS, Li TL, Wu XJ, Yang M. Local administration with tauroursodeoxycholic acid could improve osseointegration of hydroxyapatite-coated titanium implants in ovariectomized rats. J Biomater Appl 2021; 36:552-561. [PMID: 34162236 DOI: 10.1177/08853282211027678] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Despite advances in the pathogenesis of Tauroursodeoxycholic acid (TUDCA) on bone, the understanding of the effects and mechanisms of bone osseointegration in TUDCA-associated Hydroxyapatite (HA)-coated titanium implants remains poor. Therefore, the present work was aimed to evaluate the effect of local administration with TUDCA on HA-coated titanium implants osseointegration in ovariectomized(OVX) rats and further investigation of the possible mechanism. Twelve weeks after bilateral ovariectomy, all animals were randomly divided into three groups: sham operation(Sham) group, OVX group and TUDCA group, and all the rats from Sham group and OVX group received HA implants and animals belonging to group TUDCA received TUDCA-HA implants until death at 12 weeks. The bilateral femurs of rats were harvested for evaluation. TUDCA increased new bone formation around the surface of titanium rods and push-out force other than group OVX. Histology, Micro-CT and biochemical analysis results showed systemic TUDCA showed positive effects than OVX group on bone formation in osteopenic rats, with beneficial effect on via activation OPG/RANKL pathway and BMP-2/Smad1 pathway and microarchitecture as well as by reducing protein expression of TNF-α and IFN-γ. The present study suggests that local use of TUDCA may bring benefits to the osseointegration of HA-coated titanium implants in patients with osteoporosis, and this effect may be related to the inhibition of inflammatory reaction and promotion of osteogenesis.
Collapse
Affiliation(s)
- Zhou-Shan Tao
- Department of Trauma Orthopedics, The First Affiliated Hospital of 74649Wannan Medical College, Yijishan Hospital, Wuhu, People's Republic of China
| | - Tian-Lin Li
- Department of Trauma Orthopedics, The First Affiliated Hospital of 74649Wannan Medical College, Yijishan Hospital, Wuhu, People's Republic of China
| | - Xing-Jing Wu
- Department of Trauma Orthopedics, The First Affiliated Hospital of 74649Wannan Medical College, Yijishan Hospital, Wuhu, People's Republic of China
| | - Min Yang
- Department of Trauma Orthopedics, The First Affiliated Hospital of 74649Wannan Medical College, Yijishan Hospital, Wuhu, People's Republic of China
| |
Collapse
|
35
|
Giori NJ, Radin J, Callahan A, Fries JA, Halilaj E, Ré C, Delp SL, Shah NH, Harris AHS. Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries. JAMA Netw Open 2021; 4:e211728. [PMID: 33720372 PMCID: PMC7961313 DOI: 10.1001/jamanetworkopen.2021.1728] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
IMPORTANCE Implant registries provide valuable information on the performance of implants in a real-world setting, yet they have traditionally been expensive to establish and maintain. Electronic health records (EHRs) are widely used and may include the information needed to generate clinically meaningful reports similar to a formal implant registry. OBJECTIVES To quantify the extractability and accuracy of registry-relevant data from the EHR and to assess the ability of these data to track trends in implant use and the durability of implants (hereafter referred to as implant survivorship), using data stored since 2000 in the EHR of the largest integrated health care system in the United States. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of a large EHR of veterans who had 45 351 total hip arthroplasty procedures in Veterans Health Administration hospitals from 2000 to 2017. Data analysis was performed from January 1, 2000, to December 31, 2017. EXPOSURES Total hip arthroplasty. MAIN OUTCOMES AND MEASURES Number of total hip arthroplasty procedures extracted from the EHR, trends in implant use, and relative survivorship of implants. RESULTS A total of 45 351 total hip arthroplasty procedures were identified from 2000 to 2017 with 192 805 implant parts. Data completeness improved over the time. After 2014, 85% of prosthetic heads, 91% of shells, 81% of stems, and 85% of liners used in the Veterans Health Administration health care system were identified by part number. Revision burden and trends in metal vs ceramic prosthetic femoral head use were found to reflect data from the American Joint Replacement Registry. Recalled implants were obvious negative outliers in implant survivorship using Kaplan-Meier curves. CONCLUSIONS AND RELEVANCE Although loss to follow-up remains a challenge that requires additional attention to improve the quantitative nature of calculated implant survivorship, we conclude that data collected during routine clinical care and stored in the EHR of a large health system over 18 years were sufficient to provide clinically meaningful data on trends in implant use and to identify poor implants that were subsequently recalled. This automated approach was low cost and had no reporting burden. This low-cost, low-overhead method to assess implant use and performance within a large health care setting may be useful to internal quality assurance programs and, on a larger scale, to postmarket surveillance of implant performance.
Collapse
Affiliation(s)
- Nicholas J. Giori
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California
- Department of Orthopedic Surgery, Stanford University, Stanford, California
| | - John Radin
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California
| | - Alison Callahan
- Center for Biomedical Informatics Research, Stanford University, Stanford, California
| | - Jason A. Fries
- Center for Biomedical Informatics Research, Stanford University, Stanford, California
- Department of Computer Science, Stanford University, Stanford, California
| | - Eni Halilaj
- Department of Bioengineering, Stanford University, Stanford, California
| | - Christopher Ré
- Department of Computer Science, Stanford University, Stanford, California
| | - Scott L. Delp
- Department of Bioengineering, Stanford University, Stanford, California
| | - Nigam H. Shah
- Center for Biomedical Informatics Research, Stanford University, Stanford, California
| | - Alex H. S. Harris
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California
- Department of Surgery, Stanford University, Stanford, California
| |
Collapse
|