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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.
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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
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Salman LA, Khatkar H, Al-Ani A, Alzobi OZ, Abudalou A, Hatnouly AT, Ahmed G, Hameed S, AlAteeq Aldosari M. Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review. EUROPEAN JOURNAL OF ORTHOPAEDIC SURGERY & TRAUMATOLOGY : ORTHOPEDIE TRAUMATOLOGIE 2024; 34:747-756. [PMID: 38010443 PMCID: PMC10858112 DOI: 10.1007/s00590-023-03784-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/01/2023] [Indexed: 11/29/2023]
Abstract
PURPOSE This systematic review aimed to investigate the reliability of AI predictive models of intraoperative implant sizing in total knee arthroplasty (TKA). METHODS Four databases were searched from inception till July 2023 for original studies that studied the reliability of AI prediction in TKA. The primary outcome was the accuracy ± 1 size. This review was conducted per PRISMA guidelines, and the risk of bias was assessed using the MINORS criteria. RESULTS A total of four observational studies comprised of at least 34,547 patients were included in this review. A mean MINORS score of 11 out of 16 was assigned to the review. All included studies were published between 2021 and 2022, with a total of nine different AI algorithms reported. Among these AI models, the accuracy of TKA femoral component sizing prediction ranged from 88.3 to 99.7% within a deviation of one size, while tibial component sizing exhibited an accuracy ranging from 90 to 99.9% ± 1 size. CONCLUSION This study demonstrated the potential of AI as a valuable complement for planning TKA, exhibiting a satisfactory level of reliability in predicting TKA implant sizes. This predictive accuracy is comparable to that of the manual and digital templating techniques currently documented in the literature. However, future research is imperative to assess the impact of AI on patient care and cost-effectiveness. LEVEL OF EVIDENCE III PROSPERO registration number: CRD42023446868.
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Affiliation(s)
- Loay A Salman
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar.
| | | | - Abdallah Al-Ani
- Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan
| | - Osama Z Alzobi
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Abedallah Abudalou
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Ashraf T Hatnouly
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Ghalib Ahmed
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Shamsi Hameed
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Mohamed AlAteeq Aldosari
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
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Salguero JSL, Rendón MR, Valencia JT, Gil JAC, Galvis CAN, Londoño OM, Calderón CLL, Osorio FAG, Soto RT. Automatic detection of Cryptosporidium in optical microscopy images using YOLOv5 x: a comparative study. Biochem Cell Biol 2023; 101:538-549. [PMID: 37586108 DOI: 10.1139/bcb-2023-0059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023] Open
Abstract
Here, a machine learning tool (YOLOv5) enables the detection of Cryptosporidium microorganisms using optical and phase contrast microscope images. The two databases were processed using 520 images (optical microscopy) and 1200 images (phase contrast microscopy). It used Python libraries to label, standardize the size, and crop the images to generate the input tensors to the YOLOv5 network (s, m, and l). It implemented two experiments using randomly initialized weights in optical and phase contrast microscope images. The other two experiments used the parameters for the best training time obtained before and after retraining the models. Metrics used to assess model accuracy were mean average accuracy, confusion matrix, and the F1 scores. All three metrics confirmed that the optimal model used the best epoch of optical imaging training and retraining with phase contrast imaging. Experiments with randomly initialized weights with optical imaging showed the lowest precision for Cryptosporidium detection. The most stable model was YOLOv5m, with the best results in all categories. However, the differences between all models are lower than 2%, and YOLOv5s is the best option for Cryptosporidium detection considering the differences in computational costs of the models.
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Affiliation(s)
- Johan Sebastian Lopez Salguero
- Departamento de Física y Matemáticas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
- Departamento de Ciencias Básicas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - Melissa Rodríguez Rendón
- Departamento de Física y Matemáticas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
- Departamento de Ciencias Básicas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - Jessica Triviño Valencia
- Departamento de Ciencias Básicas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - Jorge Andrés Cuellar Gil
- Departamento de Ciencias Básicas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - Carlos Andrés Naranjo Galvis
- Departamento de Ciencias Básicas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - Oscar Moscoso Londoño
- Departamento de Física y Matemáticas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - César Leandro Londoño Calderón
- Departamento de Física y Matemáticas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | | | - Reinel Tabares Soto
- Departamento de Electrónica y Automatización Industrial, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
- Departamento de Sistemas e Informática, Universidad de Caldas, Manizales, CP 170001, Caldas, Colombia
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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.
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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
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Qiao Y, Li F, Zhang L, Song X, Yu X, Zhang H, Liu P, Zhou S. A systematic review and meta-analysis comparing outcomes following total knee arthroplasty for rheumatoid arthritis versus for osteoarthritis. BMC Musculoskelet Disord 2023; 24:484. [PMID: 37312069 DOI: 10.1186/s12891-023-06601-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/03/2023] [Indexed: 06/15/2023] Open
Abstract
PURPOSE Total knee arthroplasty (TKA) in patients with osteoarthritis (OA) are considered to be a successful procedure, but with little being known about outcomes in patients with rheumatoid arthritis (RA). The aim of this study was to compare the outcomes of TKA in patients with RA versus OA. METHODS Data were obtained from PubMed, Cochrane Library, EBSCO and Scopus for all available studies comparing the outcomes of THA in RA and OA patients (From January 1, 2000 to October 15, 2022). Outcomes of interest included infection, revision, venous thromboembolism (VTE), mortality, periprosthetic fractures, prosthetic loosening, length of stay, and satisfaction. Two reviewers independently assessed each study for quality and extracted data. The quality of the studies was scored using the Newcastle-Ottawa scale (NOS). RESULTS Twenty-four articles with a total 8,033,554 patients were included in this review. The results found strong evidence for increased risk of overall infection (OR = 1.61, 95% CI, 1.24-2.07; P = 0.0003), deep infection (OR = 2.06, 95% CI, 1.37-3.09; P = 0.0005), VTE (OR = 0.76, 95% CI, 0.61-0.93; P = 0.008), pulmonary embolism (PE) (OR = 0.84, 95% CI, 0.78-0.90; P<0.00001), periprosthetic fractures (OR = 1.87, 95% CI, 1.60-2.17; P<0.00001); and reasonable evidence for increased risk of deep venous thrombosis (DVT) (OR = 0.74, 95% CI, 0.54-0.99; P = 0.05), and length of stay (OR = 0.07, 95% CI, 0.01-0.14; P = 0.03) after TKA in patients with RA versus OA. There were no significant differences in superficial site infection (OR = 0.84,95% CI, 0.47-1.52; P = 0.57), revision (OR = 1.33,95% CI, 0.79-2.23; P = 0.28), mortality (OR = 1.16,95% CI, 0.87-1.55; P = 0.32), and prosthetic loosening (OR = 1.75, 95% CI, 0.56-5.48; P = 0.34) between the groups. CONCLUSION Our study demonstrated that patients with RA have a higher risk of postoperative infection, VTE, periprosthetic fracture, and lengths of stay, but did not increase revision rate, prosthetic loosening and mortality compared to patients with OA following TKA. In conclusion, despite RA increased incidence of postoperative complications, TKA should continue to be presented as an effective surgical procedure for patients whose conditions are intractable to conservative and medical management of RA.
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Affiliation(s)
- Yongjie Qiao
- Department of Joint Surgery, The 940th Hospital of Joint Logistic Support Force of Chinese People's Liberation Army, Gansu, Lanzhou, China
| | - Feng Li
- Department of Joint Surgery, The 940th Hospital of Joint Logistic Support Force of Chinese People's Liberation Army, Gansu, Lanzhou, China
- Department of Orthopedics, The 943rd Hospital of Joint Logistic Support Force of Chinese People's Liberation Army, Gansu, Wuwei, China
| | - Lvdan Zhang
- Department of Respiratory Medicine, The 940th Hospital of Joint Logistic Support Force of Chinese People's Liberation Army, Gansu, Lanzhou, China
| | - Xiaoyang Song
- Department of Joint Surgery, The 940th Hospital of Joint Logistic Support Force of Chinese People's Liberation Army, Gansu, Lanzhou, China
| | - Xinyuan Yu
- Department of Joint Surgery, The 940th Hospital of Joint Logistic Support Force of Chinese People's Liberation Army, Gansu, Lanzhou, China
| | - Haoqiang Zhang
- Department of Joint Surgery, The 940th Hospital of Joint Logistic Support Force of Chinese People's Liberation Army, Gansu, Lanzhou, China
| | - Peng Liu
- Department of Joint Surgery, The 940th Hospital of Joint Logistic Support Force of Chinese People's Liberation Army, Gansu, Lanzhou, China
| | - Shenghu Zhou
- Department of Joint Surgery, The 940th Hospital of Joint Logistic Support Force of Chinese People's Liberation Army, Gansu, Lanzhou, China.
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Hida M, Eto S, Wada C, Kitagawa K, Imaoka M, Nakamura M, Imai R, Kubo T, Inoue T, Sakai K, Orui J, Tazaki F, Takeda M, Hasegawa A, Yamasaka K, Nakao H. Development of Hallux Valgus Classification Using Digital Foot Images with Machine Learning. Life (Basel) 2023; 13:life13051146. [PMID: 37240791 DOI: 10.3390/life13051146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/03/2023] [Accepted: 05/07/2023] [Indexed: 05/28/2023] Open
Abstract
Hallux valgus, a frequently seen foot deformity, requires early detection to prevent it from becoming more severe. It is a medical economic problem, so a means of quickly distinguishing it would be helpful. We designed and investigated the accuracy of an early version of a tool for screening hallux valgus using machine learning. The tool would ascertain whether patients had hallux valgus by analyzing pictures of their feet. In this study, 507 images of feet were used for machine learning. Image preprocessing was conducted using the comparatively simple pattern A (rescaling, angle adjustment, and trimming) and slightly more complicated pattern B (same, plus vertical flip, binary formatting, and edge emphasis). This study used the VGG16 convolutional neural network. Pattern B machine learning was more accurate than pattern A. In our early model, Pattern A achieved 0.62 for accuracy, 0.56 for precision, 0.94 for recall, and 0.71 for F1 score. As for Pattern B, the scores were 0.79, 0.77, 0.96, and 0.86, respectively. Machine learning was sufficiently accurate to distinguish foot images between feet with hallux valgus and normal feet. With further refinement, this tool could be used for the easy screening of hallux valgus.
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Affiliation(s)
- Mitsumasa Hida
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Shinji Eto
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Hibikino 2-4, Wakamatsu-ku, Kitakyushu 808-0135, Japan
| | - Chikamune Wada
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Hibikino 2-4, Wakamatsu-ku, Kitakyushu 808-0135, Japan
| | - Kodai Kitagawa
- Department of Industrial Systems Engineering, National Institute of Technology, Hachinohe College, 16-1 Uwanotai, Tamonoki, Hachinohe 039-1192, Japan
| | - Masakazu Imaoka
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Misa Nakamura
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Ryota Imai
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Takanari Kubo
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Takao Inoue
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Keiko Sakai
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Junya Orui
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Fumie Tazaki
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Masatoshi Takeda
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Ayuna Hasegawa
- Department of Rehabilitation, Takata-Kamitani Hospital, Kamiyamaguchi 4-26-14, Yamaguchi, Nishinomiya 651-1421, Japan
| | - Kota Yamasaka
- Department of Rehabilitation, Takata-Kamitani Hospital, Kamiyamaguchi 4-26-14, Yamaguchi, Nishinomiya 651-1421, Japan
| | - Hidetoshi Nakao
- Department of Physical Therapy, Josai International University, 1 Gumyo, Togane 283-8555, Japan
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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.
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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
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