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Alzubaidi L, Al-Dulaimi K, Salhi A, Alammar Z, Fadhel MA, Albahri AS, Alamoodi AH, Albahri OS, Hasan AF, Bai J, Gilliland L, Peng J, Branni M, Shuker T, Cutbush K, Santamaría J, Moreira C, Ouyang C, Duan Y, Manoufali M, Jomaa M, Gupta A, Abbosh A, Gu Y. Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion. Artif Intell Med 2024; 155:102935. [PMID: 39079201 DOI: 10.1016/j.artmed.2024.102935] [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] [Received: 06/01/2023] [Revised: 03/18/2024] [Accepted: 07/22/2024] [Indexed: 08/24/2024]
Abstract
Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.
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Affiliation(s)
- Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia.
| | - Khamael Al-Dulaimi
- Computer Science Department, College of Science, Al-Nahrain University, Baghdad, Baghdad 10011, Iraq; School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Asma Salhi
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Zaenab Alammar
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Mohammed A Fadhel
- Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - A S Albahri
- Technical College, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
| | - A H Alamoodi
- Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000, Malaysia
| | - O S Albahri
- Australian Technical and Management College, Melbourne, Australia
| | - Amjad F Hasan
- Faculty of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jinshuai Bai
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Luke Gilliland
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Jing Peng
- Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Marco Branni
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Tristan Shuker
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Kenneth Cutbush
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Jose Santamaría
- Department of Computer Science, University of Jaén, Jaén 23071, Spain
| | - Catarina Moreira
- Data Science Institute, University of Technology Sydney, Australia
| | - Chun Ouyang
- School of Information Systems, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Ye Duan
- School of Computing, Clemson University, Clemson, 29631, SC, USA
| | - Mohamed Manoufali
- CSIRO, Kensington, WA 6151, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
| | - Mohammad Jomaa
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Ashish Gupta
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Amin Abbosh
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
| | - Yuantong Gu
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
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Longo UG, De Salvatore S, Valente F, Villa Corta M, Violante B, Samuelsson K. Artificial intelligence in total and unicompartmental knee arthroplasty. BMC Musculoskelet Disord 2024; 25:571. [PMID: 39034416 PMCID: PMC11265144 DOI: 10.1186/s12891-024-07516-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 05/13/2024] [Indexed: 07/23/2024] Open
Abstract
The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges with the potential to improve patient-centered decision-making and outcome prediction in orthopedics, as ML algorithms can generate patient-specific risk models. This review aims to evaluate the potential of the application of AI/ML models in the prediction of TKA outcomes and the identification of populations at risk.An extensive search in the following databases: MEDLINE, Scopus, Cinahl, Google Scholar, and EMBASE was conducted using the PIOS approach to formulate the research question. The PRISMA guideline was used for reporting the evidence of the data extracted. A modified eight-item MINORS checklist was employed for the quality assessment. The databases were screened from the inception to June 2022.Forty-four out of the 542 initially selected articles were eligible for the data analysis; 5 further articles were identified and added to the review from the PUBMED database, for a total of 49 articles included. A total of 2,595,780 patients were identified, with an overall average age of the patients of 70.2 years ± 7.9 years old. The five most common AI/ML models identified in the selected articles were: RF, in 38.77% of studies; GBM, in 36.73% of studies; ANN in 34.7% of articles; LR, in 32.65%; SVM in 26.53% of articles.This systematic review evaluated the possible uses of AI/ML models in TKA, highlighting their potential to lead to more accurate predictions, less time-consuming data processing, and improved decision-making, all while minimizing user input bias to provide risk-based patient-specific care.
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Affiliation(s)
- Umile Giuseppe Longo
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, 200 - 00128, Italy.
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy.
| | - Sergio De Salvatore
- IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
- Orthopedic Unit, Department of Surgery, Bambino Gesù Children's Hospital, Rome, Italy
| | - Federica Valente
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy
| | - Mariajose Villa Corta
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy
| | - Bruno Violante
- Orthopaedic Department, Clinical Institute Sant'Ambrogio, IRCCS - Galeazzi, Milan, Italy
| | - Kristian Samuelsson
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy
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朱 光, 邹 丽, 金 群. [Comparison of the effects of sciatic nerve block combined with continuted femoral nerve block or continuted adductor canal block on pain and motor function after total knee arthroplasty]. ZHONGGUO XIU FU CHONG JIAN WAI KE ZA ZHI = ZHONGGUO XIUFU CHONGJIAN WAIKE ZAZHI = CHINESE JOURNAL OF REPARATIVE AND RECONSTRUCTIVE SURGERY 2024; 38:556-561. [PMID: 38752241 PMCID: PMC11096886 DOI: 10.7507/1002-1892.202402048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/19/2024] [Indexed: 05/18/2024]
Abstract
Objective To compare the effect of sciatic nerve block (SNB) combined with continuted femoral nerve block (FNB) or continuted adductor canal block (ACB) on pain and motor function after total knee arthroplasty (TKA). Methods A total of 60 patients with TKA-treated osteoarthritis of the knee who met the selection criteria were enrolled between November 2020 and February 2021 and randomised allocated into the study group (SNB combined with continuted ACB) and the control group (SNB combined with continuted FNB), with 30 cases in each group. There was no significant difference in gender, age, body mass, height, body mass index, preoperative Hospital for Special Surgery (HSS) score, femoral tibial angle, and medial proximal tibial angle between the two groups ( P>0.05). The operation time, the initial time to the ground, the initial walking distance, and the postoperative hospital stay were recorded. At 2, 4, 6, 12, 24, and 48 hours after operation, the numerical rating scale (NRS) score was used to evaluate the rest pain around the knee joint, the quadriceps femoris muscle strength was evaluated by the freehand muscle strength method, and the knee flexion and extension angles were measured. Results There was no significant difference in the operation time and initial walking distance between the two groups ( P>0.05); the initial time to the ground and postoperative hospital stay of the study group were significantly shorter than those of the control group ( P<0.05). Except for the 48-hour postoperative NRS score of the study group, which was significantly lower than that of the control group ( P<0.05), there was no significant difference in the NRS scores between the two groups at the remaining time points ( P>0.05). The quadriceps femoris muscle strength from 4 to 24 hours postoperatively and the knee extension angle from 2 to 6 hours postoperatively of the study group were significantly better than those of the control group ( P<0.05); the differences in the quadriceps femoris muscle strength and knee extension and flexion angles between the two groups at the remaining time points were not significant ( P>0.05). Conclusion SNB combined with either continuted ACB or continuted FNB can effectively relieve pain in patients after TKA, and compared with combined continuted FNB, combined continuted ACB has less effect on quadriceps femoris muscle strength, and patients have better recovery of knee flexion and extension mobility.
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Affiliation(s)
- 光 朱
- 宁夏医科大学临床医学院(银川 750001)School of Clinical Medicine, Ningxia Medical University, Yinchuan Ningxia, 750001, P. R. China
| | - 丽丽 邹
- 宁夏医科大学临床医学院(银川 750001)School of Clinical Medicine, Ningxia Medical University, Yinchuan Ningxia, 750001, P. R. China
| | - 群华 金
- 宁夏医科大学临床医学院(银川 750001)School of Clinical Medicine, Ningxia Medical University, Yinchuan Ningxia, 750001, P. R. China
- 宁夏医科大学总医院麻醉科(银川 750001)Department of Anesthesiology, General Hospital of Ningxia Medical University, Yinchuan Ningxia, 750001, P. R. China
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Adams LC, Bressem KK, Ziegeler K, Vahldiek JL, Poddubnyy D. Artificial intelligence to analyze magnetic resonance imaging in rheumatology. Joint Bone Spine 2024; 91:105651. [PMID: 37797827 DOI: 10.1016/j.jbspin.2023.105651] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/29/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.
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Affiliation(s)
- Lisa C Adams
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Keno K Bressem
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Katharina Ziegeler
- Department of Hematology, Oncology , and Cancer Immunology, Campus Charité Mitte, Charité Universitätsmedizin Berlin, Germany; Evidia Radiologie am Rheumazentrum Ruhrgebiet, Germany
| | - Janis L Vahldiek
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Denis Poddubnyy
- Department of Gastroenterology, Infectious Diseases and Rheumatology (including Nutrition Medicine), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
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Turgut N, Beyaz S. The 100 most cited articles in artificial intelligence related to orthopedics. Front Surg 2024; 11:1370335. [PMID: 38712339 PMCID: PMC11072182 DOI: 10.3389/fsurg.2024.1370335] [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: 01/14/2024] [Accepted: 04/04/2024] [Indexed: 05/08/2024] Open
Abstract
Background This bibliometric study aimed to identify and analyze the top 100 articles related to artificial intelligence in the field of orthopedics. Methods The articles were assessed based on their number of citations, publication years, countries, journals, authors, affiliations, and funding agencies. Additionally, they were analyzed in terms of their themes and objectives. Keyword co-occurrence, co-citation of authors, and co-citation of references analyses were conducted using VOSviewer (version 1.6.19). Results The number of citations of these articles ranged from 32 to 272, with six papers having more than 200 citations The years of 2019 (n: 37) and 2020 (n: 19) together constituted 56% of the list. The USA was the leading contributor country to this field (n: 61). The most frequently used keywords were "machine learning" (n: 26), "classification" (n: 18), "deep learning" (n: 16), "artificial intelligence" (n: 14), respectively. The most common themes were decision support (n: 25), fracture detection (n: 24), and osteoarthrtitis staging (n: 21). The majority of the studies were diagnostic in nature (n: 85), with only two articles focused on treatment. Conclusions This study provides valuable insights and presents the historical perspective of scientific development on artificial intelligence in the field of orthopedics. The literature in this field is expanding rapidly. Currently, research is generally done for diagnostic purposes and predominantly focused on decision support systems, fracture detection, and osteoarthritis classification.
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Affiliation(s)
- Necmettin Turgut
- Department of Orthopedics and Traumatology, Adana Turgut Noyan Research and Training Centre, Başkent University, Adana, Türkiye
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Zhou Y, Patten L, Spelman T, Bunzli S, Choong PFM, Dowsey MM, Schilling C. Predictive Tool Use and Willingness for Surgery in Patients With Knee Osteoarthritis: A Randomized Clinical Trial. JAMA Netw Open 2024; 7:e240890. [PMID: 38457182 PMCID: PMC10924247 DOI: 10.1001/jamanetworkopen.2024.0890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/11/2024] [Indexed: 03/09/2024] Open
Abstract
Importance Despite the increasing number of tools available to predict the outcomes of total knee arthroplasty (TKA), the effect of these predictive tools on patient decision-making remains uncertain. Objective To assess the effect of an online predictive tool on patient-reported willingness to undergo TKA. Design, Setting, and Participants This parallel, double-masked, 2-arm randomized clinical trial compared predictive tool use with treatment as usual (TAU). The study was conducted between June 30, 2022, and July 31, 2023. Participants were followed up for 6 months after enrollment. Participants were recruited from a major Australian private health insurance company and from the surgical waiting list for publicly funded TKA at a tertiary hospital. Eligible participants had unilateral knee osteoarthritis, were contemplating TKA, and had previously tried nonsurgical interventions, such as lifestyle modifications, physiotherapy, and pain medications. Intervention The intervention group was provided access to an online predictive tool at the beginning of the study. This tool offered information regarding the likelihood of improvement in quality of life if patients chose to undergo TKA. The predictions were based on the patient's age, sex, and baseline symptoms. Conversely, the control group received TAU without access to the predictive tool. Main Outcomes and Measures The primary outcome measure was the reduction in participants' willingness to undergo surgery at 6 months after tool use as measured by binomial logistic regression. Secondary outcome measures included participant treatment preference and the quality of their decision-making process as measured by the Knee Decision Quality Instrument. Results Of 211 randomized participants (mean [SD] age, 65.8 [8.3] years; 118 female [55.9%]), 105 were allocated to the predictive tool group and 106 to the TAU group. After adjusting for baseline differences in willingness for surgery, the predictive tool did not significantly reduce the primary outcome of willingness for surgery at 6 months (adjusted odds ratio, 0.85; 95% CI, 0.42-1.71; P = .64). Conclusions and Relevance Despite the absence of treatment effect on willingness for TKA, predictive tools might still enhance health outcomes of patients with knee osteoarthritis. Additional research is needed to optimize the design and implementation of predictive tools, address limitations, and fully understand their effect on the decision-making process in TKA. Trial Registration ANZCTR.org.au Identifier: ACTRN12622000072718.
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Affiliation(s)
- Yushy Zhou
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Orthopaedic Surgery, St Vincent’s Hospital, Melbourne, Victoria, Australia
| | - Lauren Patten
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Orthopaedic Surgery, St Vincent’s Hospital, Melbourne, Victoria, Australia
| | - Tim Spelman
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Samantha Bunzli
- School of Health Sciences and Social Work, Griffith University, Nathan Campus, Brisbane, Queensland, Australia
- Physiotherapy Department, Royal Brisbane and Women’s Hospital, Brisbane, Queensland, Australia
| | - Peter F. M. Choong
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Michelle M. Dowsey
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Orthopaedic Surgery, St Vincent’s Hospital, Melbourne, Victoria, Australia
| | - Chris Schilling
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
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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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Minnig MCC, Golightly YM, Nelson AE. Epidemiology of osteoarthritis: literature update 2022-2023. Curr Opin Rheumatol 2024; 36:108-112. [PMID: 38240280 PMCID: PMC10965245 DOI: 10.1097/bor.0000000000000985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
PURPOSE OF REVIEW This review highlights recently published studies on osteoarthritis (OA) epidemiology, including topics related to understudied populations and joints, imaging, and advancements in artificial intelligence (AI) methods. RECENT FINDINGS Contemporary research has improved our understanding of the burden of OA in typically understudied regions, including ethnic and racial minorities in high-income countries, the Middle East and North Africa (MENA) and Latin America. Efforts have also been made to explore the burden and risk factors in OA in previously understudied joints, such as the hand, foot, and ankle. Advancements in OA imaging techniques have occurred alongside the developments of AI methods aiming to predict disease phenotypes, progression, and outcomes. SUMMARY Continuing efforts to expand our knowledge around OA in understudied populations will allow for the creation of targeted and specific interventions and inform policy changes aimed at reducing disease burden in these groups. The burden and disability associated with OA is notable in understudied joints, warranting further research efforts that may lead to effective therapeutic options. AI methods show promising results of predicting OA phenotypes and progression, which also may encourage the creation of targeted disease modifying OA drugs (DMOADs).
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Affiliation(s)
- Mary Catherine C. Minnig
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yvonne M. Golightly
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, North Carolina, USA
- College of Allied Health Professions, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Amanda E. Nelson
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Medicine, University of North Carolina, Chapel Hill, NC, USA
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Bumann EE, Al-Qarni S, Chandrashekar G, Sabzian R, Bohaty B, Lee Y. A novel collaborative learning model for mixed dentition and fillings segmentation in panoramic radiographs. J Dent 2024; 140:104779. [PMID: 38007173 DOI: 10.1016/j.jdent.2023.104779] [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] [Received: 03/10/2023] [Revised: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 11/27/2023] Open
Abstract
INTRODUCTION It is critical for dentists to identify and differentiate primary and permanent teeth, fillings, dental restorations and areas with pathological findings when reviewing dental radiographs to ensure that an accurate diagnosis is made and the optimal treatment can be planned. Unfortunately, dental radiographs are sometimes read incorrectly due to human error or low-quality images. While secondary or group review can help catch errors, many dentists work in practice alone and/or do not have time to review all of their patients' radiographs with another dentist. Artificial intelligence may facilitate the accurate interpretation of radiographs. To help support the review of panoramic radiographs, we developed a novel collaborative learning model that simultaneously identifies and differentiates primary and permanent teeth and detects fillings. METHODS We used publicly accessible dental panoramic radiographic images and images obtained from the University of Missouri-Kansas City, School of Dentistry to develop and optimize two high-performance classifiers: (1) a system for tooth segmentation that can differentiate primary and permanent teeth and (2) a system to detect dental fillings. RESULTS By utilizing these high-performance classifiers, we created models that can identify primary and permanent teeth (mean average precision [mAP] 95.32 % and performance [F-1] 92.50 %), as well as their associated dental fillings (mAP 91.53 % and F-1 91.00 %). We also designed a novel method for collaborative learning that utilizes these two classifiers to enhance recognition performance (mAP 94.09 % and F-1 93.41 %). CONCLUSIONS Our model improves upon the existing machine learning models to simultaneously identify and differentiate primary and permanent teeth, and to identify any associated fillings. CLINICAL SIGNIFICANCE Human error can lead to incorrect readings of panoramic radiographs. By developing artificial intelligence and machine learning methods to analyze panoramic radiographs, dentists can use this information to support their radiograph interpretations, help communicate the information to patients, and assist dental students learning to read radiographs.
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Affiliation(s)
- Erin Ealba Bumann
- Department of Oral and Craniofacial Sciences, University of Missouri-Kansas City, USA.
| | - Saeed Al-Qarni
- Department of Computer Science, University of Missouri-Kansas City, USA; Department of Computing and Informatics, Saudi Electronic University, Saudi Arabia
| | | | - Roya Sabzian
- Department of Oral and Craniofacial Sciences, University of Missouri-Kansas City, USA
| | - Brenda Bohaty
- Department of Pediatric Dentistry, University of Missouri-Kansas City, USA; Department of Pediatric Dentistry, Children's Mercy Hospital, USA
| | - Yugyung Lee
- Department of Computer Science, University of Missouri-Kansas City, USA
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10
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Maimaiti Z, Li Z, Li Z, Fu J, Xu C, Chen J, Chai W, Liu L. Ortho-digital dynamics: Exploration of advancing digital health technologies in musculoskeletal disease management. Digit Health 2024; 10:20552076241269613. [PMID: 39148814 PMCID: PMC11325473 DOI: 10.1177/20552076241269613] [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: 02/06/2024] [Accepted: 06/27/2024] [Indexed: 08/17/2024] Open
Abstract
Background Musculoskeletal (MSK) disorders, affecting billions of people worldwide, pose significant challenges to the healthcare system and require effective management models. The rapid development of digital healthcare technologies (DHTs) has revolutionized the healthcare industry. DHT-based interventions have shown promising clinical benefits in managing MSK disorders, alleviating pain, and improving functional impairment. There is, however, no bibliometric analysis of the overall trends on this topic. Methods We extracted all relevant publications from the Web of Science Core Collection (WoSCC) database until April 30, 2023. We performed bibliometric analysis and visualization using CiteSpace, VOSviewer, and R software. Annual trends of publications, countries/regions distributions, funding agencies, institutions, co-cited journals, author contributions, references, core journals, and keywords and research hotspots were analyzed. Results A total of 6810 papers were enrolled in this study. Publications have increased drastically from 16 in 1995 to 1198 in 2022, with 4067 articles published in the last five years. In all, 53 countries contributed with publications to this research area. The United States, the United Kingdom, and China were the most productive countries. Harvard University was the most contributing institution. Regarding keywords, research focuses include artificial intelligence, deep learning, machine learning, telemedicine, rehabilitation, and robotics. Conclusion The COVID-19 pandemic has further accelerated the adoption of DHTs, highlighting the need for remote care options. The analysis reveals the positive impact of DHTs on improving physician productivity, enhancing patient care and quality of life, reducing healthcare expenditures, and predicting outcomes. DHTs are a hot topic of research not only in the clinical field but also in the multidisciplinary intersection of rehabilitation, nursing, education, social and economic fields. The analysis identifies four promising hotspots in the integration of DHTs in MSK pain management, biomechanics assessment, MSK diagnosis and prediction, and robotics and tele-rehabilitation in arthroplasty care.
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Affiliation(s)
- Zulipikaer Maimaiti
- Department of Orthopedics, Beijing Luhe Hospital, Capital Medical University, Beijing, China
- Senior Department of Orthopaedics, The Fourth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhuo Li
- Department of Joint Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Zhiyuan Li
- Senior Department of Orthopaedics, The Fourth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Jun Fu
- Senior Department of Orthopaedics, The Fourth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Chi Xu
- Senior Department of Orthopaedics, The Fourth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Jiying Chen
- Senior Department of Orthopaedics, The Fourth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Wei Chai
- Senior Department of Orthopaedics, The Fourth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Liang Liu
- Department of Orthopedics, Beijing Luhe Hospital, Capital Medical University, Beijing, China
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11
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Migliorini F, Feierabend M, Hofmann UK. Fostering Excellence in Knee Arthroplasty: Developing Optimal Patient Care Pathways and Inspiring Knowledge Transfer of Advanced Surgical Techniques. J Healthc Leadersh 2023; 15:327-338. [PMID: 38020721 PMCID: PMC10676205 DOI: 10.2147/jhl.s383916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/13/2023] [Indexed: 12/01/2023] Open
Abstract
Osteoarthritis of the knee is common. Early sports trauma or cartilage defects are risk factors for osteoarthritis. If conservative treatment fails, partial or total joint replacement is often performed. A joint replacement aims to restore physiological biomechanics and the quality of life of affected patients. Total knee arthroplasty is one of the most performed surgeries in musculoskeletal medicine. Several developments have taken place over the last decades that have truly altered the way we look at knee arthroplasty today. Some of the fascinating aspects will be presented and discussed in the present narrative review.
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Affiliation(s)
- Filippo Migliorini
- Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Medical Centre, Aachen, 52074, Germany
- Department of Orthopedics and Trauma Surgery, Academic Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University, 39100 Bolzano, Italy
| | - Martina Feierabend
- Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Medical Centre, Aachen, 52074, Germany
| | - Ulf Krister Hofmann
- Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Medical Centre, Aachen, 52074, Germany
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12
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Shah AK, Lavu MS, Hecht CJ, Burkhart RJ, Kamath AF. Understanding the use of artificial intelligence for implant analysis in total joint arthroplasty: a systematic review. ARTHROPLASTY 2023; 5:54. [PMID: 37919812 PMCID: PMC10623774 DOI: 10.1186/s42836-023-00209-z] [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: 08/03/2023] [Accepted: 09/01/2023] [Indexed: 11/04/2023] Open
Abstract
INTRODUCTION In recent years, there has been a significant increase in the development of artificial intelligence (AI) algorithms aimed at reviewing radiographs after total joint arthroplasty (TJA). This disruptive technology is particularly promising in the context of preoperative planning for revision TJA. Yet, the efficacy of AI algorithms regarding TJA implant analysis has not been examined comprehensively. METHODS PubMed, EBSCO, and Google Scholar electronic databases were utilized to identify all studies evaluating AI algorithms related to TJA implant analysis between 1 January 2000, and 27 February 2023 (PROSPERO study protocol registration: CRD42023403497). The mean methodological index for non-randomized studies score was 20.4 ± 0.6. We reported the accuracy, sensitivity, specificity, positive predictive value, and area under the curve (AUC) for the performance of each outcome measure. RESULTS Our initial search yielded 374 articles, and a total of 20 studies with three main use cases were included. Sixteen studies analyzed implant identification, two addressed implant failure, and two addressed implant measurements. Each use case had a median AUC and accuracy above 0.90 and 90%, respectively, indicative of a well-performing AI algorithm. Most studies failed to include explainability methods and conduct external validity testing. CONCLUSION These findings highlight the promising role of AI in recognizing implants in TJA. Preliminary studies have shown strong performance in implant identification, implant failure, and accurately measuring implant dimensions. Future research should follow a standardized guideline to develop and train models and place a strong emphasis on transparency and clarity in reporting results. LEVEL OF EVIDENCE Level III.
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Affiliation(s)
- Aakash K Shah
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
| | - Monish S Lavu
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
| | - Christian J Hecht
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
| | - Robert J Burkhart
- Department of Orthopaedic Surgery, University Hospitals, Cleveland, OH, 44106, USA
| | - Atul F Kamath
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA.
- Center for Hip Preservation, Orthopaedic and Rheumatologic Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Mail Code A41, Cleveland, OH, 44195, USA.
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13
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Piccolo CL, Mallio CA, Vaccarino F, Grasso RF, Zobel BB. Imaging of knee osteoarthritis: a review of multimodal diagnostic approach. Quant Imaging Med Surg 2023; 13:7582-7595. [PMID: 37969633 PMCID: PMC10644136 DOI: 10.21037/qims-22-1392] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/22/2023] [Indexed: 11/17/2023]
Abstract
Knee osteoarthritis (KOA) is a common chronic condition among the elderly population that significantly affects the quality of life. Imaging is crucial in the diagnosis, evaluation, and management of KOA. This manuscript reviews the various imaging modalities available until now, with a little focus on the recent developments with Artificial Intelligence. Currently, radiography is the first-line imaging modality recommended for the diagnosis of KOA, owing to its wide availability, affordability, and ability to provide a clear view of bony components of the knee. Although radiography is useful in assessing joint space narrowing (JSN), osteophytes and subchondral sclerosis, it has limited effectiveness in detecting early cartilage damage, soft tissue abnormalities and synovial inflammation. Ultrasound is a safe and affordable imaging technique that can provide information on cartilage thickness, synovial fluid, JSN and osteophytes, though its ability to evaluate deep structures such as subchondral bone is limited. Magnetic resonance imaging (MRI) represents the optimal imaging modality to assess soft tissue structures. New MRI techniques are able to detect early cartilage damage measuring the T1ρ and T2 relaxation time of knee cartilage. Delayed gadolinium-enhanced MRI of cartilage, by injecting a contrast agent to enhance the visibility of the cartilage on MRI scans, can provide information about its integrity. Despite these techniques can provide valuable information about the biochemical composition of knee cartilage and can help detect early signs of osteoarthritis (OA), they may not be widely available. Computed tomography (CT) has restricted utility in evaluating OA; nonetheless, weight-bearing CT imaging, using the joint space mapping technique, exhibits potential in quantifying knee joint space width and detecting structural joint ailments. PET-MRI is a hybrid imaging technique able to combine morphological information on bone and soft tissue alterations with the biochemical changes, but more research is needed to justify its high cost and time involved. The new tools of artificial intelligence, including machine learning models, can assist in detecting patterns and correlations in KOA that may be useful in the diagnosis, grading, predicting the need for arthroplasty, and improving surgical accuracy.
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Affiliation(s)
- Claudia Lucia Piccolo
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Roma, Italy
| | - Carlo Augusto Mallio
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Roma, Italy
- Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, Italy
| | - Federica Vaccarino
- Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, Italy
| | - Rosario Francesco Grasso
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Roma, Italy
- Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, Italy
| | - Bruno Beomonte Zobel
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Roma, Italy
- Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, Italy
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14
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Dhanalakshmi S, Maanasaa RS, Maalikaa RS, Senthil R. A review of emergent intelligent systems for the detection of Parkinson's disease. Biomed Eng Lett 2023; 13:591-612. [PMID: 37872986 PMCID: PMC10590348 DOI: 10.1007/s13534-023-00319-2] [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: 05/25/2023] [Revised: 08/11/2023] [Accepted: 09/07/2023] [Indexed: 10/25/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder affecting people worldwide. The PD symptoms are divided into motor and non-motor symptoms. Detection of PD is very crucial and essential. Such challenges can be overcome by applying artificial intelligence to diagnose PD. Many studies have also proposed the implementation of computer-aided diagnosis for the detection of PD. This systematic review comprehensively analyzed all appropriate algorithms for detecting and assessing PD based on the literature from 2012 to 2023 which are conducted as per PRISMA model. This review focused on motor symptoms, namely handwriting dynamics, voice impairments and gait, multimodal features, and brain observation using single photon emission computed tomography, magnetic resonance and electroencephalogram signals. The significant challenges are critically analyzed, and appropriate recommendations are provided. The critical discussion of this review article can be helpful in today's PD community in such a way that it allows clinicians to provide proper treatment and timely medication.
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Affiliation(s)
- Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
| | - Ramesh Sai Maanasaa
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
| | - Ramesh Sai Maalikaa
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
| | - Ramalingam Senthil
- Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
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15
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Tong SX, Li RS, Wang D, Xie XM, Ruan Y, Huang L. Artificial intelligence technology and ultrasound-guided nerve block for analgesia in total knee arthroplasty. World J Clin Cases 2023; 11:7026-7033. [PMID: 37946775 PMCID: PMC10631398 DOI: 10.12998/wjcc.v11.i29.7026] [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: 08/15/2023] [Revised: 09/14/2023] [Accepted: 09/22/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Knee diseases are more common in middle-aged and elderly people, so artificial knee replacement is also more used in middle-aged and elderly people. Although the patient's pain can be reduced through surgery, often accompanied by moderate pain after surgery and neutralization, which not only increases the psychological burden of the patient, but also greatly reduces the postoperative recovery effect, and may also lead to the occurrence of postoperative adverse events in severe cases. AIM To investigate the analgesic effect of artificial intelligence (AI) and ultrasound-guided nerve block in total knee arthroplasty (TKA). METHODS A total of 92 patients with TKA admitted to our hospital from January 2021 to January 2022 were opted and divided into two groups according to the treatment regimen. The control group received combined spinal-epidural anesthesia. The research group received AI technique combined with ultrasound-guided nerve block anesthesia. The sensory block time, motor block time, visual analogue scale (VAS) at different time points and complications were contrasted between the two groups. RESULTS The time of sensory block onset and sensory block perfection in the research group was shorter than those in the control group, but the results had no significant difference (P > 0.05). Duration of sensory block in the research group was significantly longer than those in the control group (P < 0.05). The time of motor block onset and motor block perfection in the research group was shorter than those in the control group, but the results had no significant difference (P > 0.05). Duration of motor block in the research group was significantly longer than those in the control group. The VAS scales of the research group were significantly lower than that of the control group at different time points (P < 0.05). The postoperative hip flexion and abduction range of motion in the research group were significantly better than those in the control group at different time points (P < 0.05). The incidence of complications was significantly lower in the research group than in the control group (P = 0.049). CONCLUSION In TKA, the combination of AI technology and ultrasound-guided nerve block has a significantly effect, with fewer postoperative complications and significantly analgesic effect, which is worthy of application.
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Affiliation(s)
- Sheng-Xiong Tong
- Department of Pain Management, Wuhan First Hospital, Wuhan 430033, Hubei Province, China
| | - Ren-Song Li
- Department of Orthopaedics, Wuhan Wuchang Hospital, Wuhan 430063, Hubei Province, China
| | - Dan Wang
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei Province, China
| | - Xiao-Meng Xie
- Department of Nursing, Huanggang Central Hospital, Huanggang 438000, Hubei Province, China
| | - Yuan Ruan
- Department of Orthopaedics, Huanggang Central Hospital, Huanggang 438000, Hubei Province, China
| | - Lin Huang
- Department of Orthopaedics, Huanggang Central Hospital, Huanggang 438000, Hubei Province, China
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Cigdem O, Deniz CM. Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022. OSTEOARTHRITIS IMAGING 2023; 3:100161. [PMID: 38948116 PMCID: PMC11213283 DOI: 10.1016/j.ostima.2023.100161] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Objective The aim of this literature review is to yield a comprehensive and exhaustive overview of the existing evidence and up-to-date applications of artificial intelligence for knee osteoarthritis. Methods A literature review was performed by using PubMed, Google Scholar, and IEEE databases for articles published in peer-reviewed journals in 2022. The articles focusing on the use of artificial intelligence in diagnosis and prognosis of knee osteoarthritis and accelerating the image acquisition were selected. For each selected study, the code availability, considered number of patients and knees, imaging type, covariates, grading type of osteoarthritis, models, validation approaches, objectives, and results were reviewed. Results 395 articles were screened, and 35 of them were reviewed. Eight articles were based on diagnosis, six on prognosis prediction, three on classification, three on accelerated image acquisition, and 15 on segmentation of knee osteoarthritis. 57% of the articles used MRI, 26% radiography, 6% MRI together with radiography, 6% ultrasonography, and 6% only clinical data. 23% of the articles made the computer codes available for their study, and 26% used clinical data. External validation and nested cross-validation were used in 17% and 14% of articles, respectively. Conclusions The use of artificial intelligence provided a promising potential to enhance the detection and management of knee osteoarthritis. Translating the developed models into clinics is still in the early stages of development. The translation of artificial intelligence models is expected to be further examined in prospective studies to support clinicians in improving routine healthcare practice.
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Affiliation(s)
- Ozkan Cigdem
- Department of Radiology, New York University Grossman School of Medicine, 227 E 30th St, 7th Floor, New York, NY 10016, United States
| | - Cem M Deniz
- Department of Radiology, New York University Grossman School of Medicine, 227 E 30th St, 7th Floor, New York, NY 10016, United States
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17
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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.
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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.
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Kamal AH, Zakaria OM, Majzoub RA, Nasir EWF. Artificial intelligence in orthopedics: A qualitative exploration of the surgeon perspective. Medicine (Baltimore) 2023; 102:e34071. [PMID: 37327255 PMCID: PMC10270518 DOI: 10.1097/md.0000000000034071] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 06/01/2023] [Indexed: 06/18/2023] Open
Abstract
Artificial intelligence (AI) is currently integrated into many medical services. AI is utilized in many aspects of orthopedic surgery. The scope ranges from diagnosis to complex surgery. To evaluate the perceptions, attitudes, and interests of Sudanese orthopedic surgeons regarding the different applications of AI in orthopedic surgery. This qualitative questionnaire-based study was conducted through an anonymous electronic survey using Google Forms distributed among Sudanese orthopedic surgeons. The questionnaire entailed 4 sections. The first section included the participants' demographic data. The remaining 3 sections included questions for the assessment of the perception, attitude, and interest of surgeons toward (AI). The validity and reliability of the questionnaire were tested and piloted before the final dissemination. One hundred twenty-nine surgeons responded to the questionnaires. Most respondents needed to be more aware of the basic concepts of AI. However, most respondents were aware of its use in spinal and joint replacement surgeries. Most respondents had doubts regarding the safety of (AI). However, they were highly interested in utilizing (AI) in many orthopedic surgical aspects. Orthopedic surgery is a rapidly evolving branch of surgery that involves adoption of new technologies. Therefore, orthopedic surgeons should be encouraged to enroll in research activities to generate more studies and reviews to assess the usefulness and safety of emerging technologies.
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Affiliation(s)
- Ahmed Hassan Kamal
- Department of Surgery, College of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia
| | | | - Rabab Abbas Majzoub
- Department of Pediatrics, College of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia
| | - El Walid Fadul Nasir
- Department of Public Health & Biostatics, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia
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Cui T, Liu R, Jing Y, Fu J, Chen J. Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis. J Orthop Surg Res 2023; 18:375. [PMID: 37210510 DOI: 10.1186/s13018-023-03837-y] [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: 01/28/2023] [Accepted: 05/06/2023] [Indexed: 05/22/2023] Open
Abstract
BACKGROUND To develop and assess the performance of machine learning (ML) models based on magnetic resonance imaging (MRI) radiomics analysis for knee osteoarthritis (KOA) diagnosis. METHODS This retrospective study analysed 148 consecutive patients (72 with KOA and 76 without) with available MRI image data, where radiomics features in cartilage portions were extracted and then filtered. Intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features, and a threshold of 0.8 was set. The training and validation cohorts consisted of 117 and 31 cases, respectively. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), K-nearest neighbour (KNN) and support vector machine (SVM). In each algorithm, ten models derived from all available planes of three joint compartments and their various combinations were, respectively, constructed for comparative analysis. The performance of classifiers was mainly evaluated and compared by receiver operating characteristic (ROC) analysis. RESULTS All models achieved satisfying performances, especially the Final model, where accuracy and area under ROC curve (AUC) of LR classifier were 0.968, 0.983 (0.957-1.000, 95% CI) in the validation cohort, and 0.940, 0.984 (0.969-0.995, 95% CI) in the training cohort, respectively. CONCLUSION The MRI radiomics analysis represented promising performance in noninvasive and preoperative KOA diagnosis, especially when considering all available planes of all three compartments of knee joints.
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Affiliation(s)
- Tingrun Cui
- Medical School of Chinese PLA, Beijing, China
- Department of Orthopaedics, The First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Ruilong Liu
- Department of Bone and Joint Surgery, Jining No. 2 People's Hospital, Jining, Shandong, China
| | - Yang Jing
- Huiying Medical Technology Co. Ltd, Beijing, China
| | - Jun Fu
- Department of Orthopaedics, The First Medical Centre of Chinese PLA General Hospital, Beijing, China.
| | - Jiying Chen
- Department of Orthopaedics, The First Medical Centre of Chinese PLA General Hospital, Beijing, China.
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Khalid A, Senan EM, Al-Wagih K, Ali Al-Azzam MM, Alkhraisha ZM. Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted. Diagnostics (Basel) 2023; 13:diagnostics13091609. [PMID: 37175000 PMCID: PMC10178472 DOI: 10.3390/diagnostics13091609] [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: 03/25/2023] [Revised: 04/25/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Knee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, affecting more than 5% of people worldwide. KOA goes through many stages, from the mild grade that can be treated to the severe grade in which the knee must be replaced. Therefore, early diagnosis of KOA is essential to avoid its development to the advanced stages. X-rays are one of the vital techniques for the early detection of knee infections, which requires highly experienced doctors and radiologists to distinguish Kellgren-Lawrence (KL) grading. Thus, artificial intelligence techniques solve the shortcomings of manual diagnosis. This study developed three methodologies for the X-ray analysis of both the Osteoporosis Initiative (OAI) and Rani Channamma University (RCU) datasets for diagnosing KOA and discrimination between KL grades. In all methodologies, the Principal Component Analysis (PCA) algorithm was applied after the CNN models to delete the unimportant and redundant features and keep the essential features. The first methodology for analyzing x-rays and diagnosing the degree of knee inflammation uses the VGG-19 -FFNN and ResNet-101 -FFNN systems. The second methodology of X-ray analysis and diagnosis of KOA grade by Feed Forward Neural Network (FFNN) is based on the combined features of VGG-19 and ResNet-101 before and after PCA. The third methodology for X-ray analysis and diagnosis of KOA grade by FFNN is based on the fusion features of VGG-19 and handcrafted features, and fusion features of ResNet-101 and handcrafted features. For an OAI dataset with fusion features of VGG-19 and handcrafted features, FFNN obtained an AUC of 99.25%, an accuracy of 99.1%, a sensitivity of 98.81%, a specificity of 100%, and a precision of 98.24%. For the RCU dataset with the fusion features of VGG-19 and the handcrafted features, FFNN obtained an AUC of 99.07%, an accuracy of 98.20%, a sensitivity of 98.16%, a specificity of 99.73%, and a precision of 98.08%.
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Affiliation(s)
- Ahmed Khalid
- Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
| | - Khalil Al-Wagih
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
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Accelerating UN Sustainable Development Goals with AI-Driven Technologies: A Systematic Literature Review of Women's Healthcare. Healthcare (Basel) 2023; 11:healthcare11030401. [PMID: 36766976 PMCID: PMC9914215 DOI: 10.3390/healthcare11030401] [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: 12/05/2022] [Revised: 01/24/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023] Open
Abstract
In this paper, we critically examine if the contributions of artificial intelligence (AI) in healthcare adequately represent the realm of women's healthcare. This would be relevant for achieving and accelerating the gender equality and health sustainability goals (SDGs) defined by the United Nations. Following a systematic literature review (SLR), we examine if AI applications in health and biomedicine adequately represent women's health in the larger scheme of healthcare provision. Our findings are divided into clusters based on thematic markers for women's health that are commensurate with the hypotheses that AI-driven technologies in women's health still remain underrepresented, but that emphasis on its future deployment can increase efficiency in informed health choices and be particularly accessible to women in small or underrepresented communities. Contemporaneously, these findings can assist and influence the shape of governmental policies, accessibility, and the regulatory environment in achieving the SDGs. On a larger scale, in the near future, we will extend the extant literature on applications of AI-driven technologies in health SDGs and set the agenda for future research.
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Neubauer M, Moser L, Neugebauer J, Raudner M, Wondrasch B, Führer M, Emprechtinger R, Dammerer D, Ljuhar R, Salzlechner C, Nehrer S. Artificial-Intelligence-Aided Radiographic Diagnostic of Knee Osteoarthritis Leads to a Higher Association of Clinical Findings with Diagnostic Ratings. J Clin Med 2023; 12:jcm12030744. [PMID: 36769394 PMCID: PMC9917552 DOI: 10.3390/jcm12030744] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Radiographic knee osteoarthritis (OA) severity and clinical severity are often dissociated. Artificial intelligence (AI) aid was shown to increase inter-rater reliability in radiographic OA diagnosis. Thus, AI-aided radiographic diagnoses were compared against AI-unaided diagnoses with regard to their correlations with clinical severity. METHODS Seventy-one DICOMs (m/f = 27:42, mean age: 27.86 ± 6.5) (X-ray format) were used for AI analysis (KOALA software, IB Lab GmbH). Subjects were recruited from a physiotherapy trial (MLKOA). At baseline, each subject received (i) a knee X-ray and (ii) an assessment of five main scores (Tegner Scale (TAS); Knee Injury and Osteoarthritis Outcome Score (KOOS); International Physical Activity Questionnaire; Star Excursion Balance Test; Six-Minute Walk Test). Clinical assessments were repeated three times (weeks 6, 12 and 24). Three physicians analyzed the presented X-rays both with and without AI via KL grading. Analyses of the (i) inter-rater reliability (IRR) and (ii) Spearman's Correlation Test for the overall KL score for each individual rater with clinical score were performed. RESULTS We found that AI-aided diagnostic ratings had a higher association with the overall KL score and the KOOS. The amount of improvement due to AI depended on the individual rater. CONCLUSION AI-guided systems can improve the ratings of knee radiographs and show a stronger association with clinical severity. These results were shown to be influenced by individual readers. Thus, AI training amongst physicians might need to be increased. KL might be insufficient as a single tool for knee OA diagnosis.
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Affiliation(s)
- Markus Neubauer
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Lukas Moser
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Johannes Neugebauer
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Marcus Raudner
- Medical University of Vienna, High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Währinger-Gürtel 18-20, 1090 Vienna, Austria
| | - Barbara Wondrasch
- Department of Health and Social Sciences, St. Poelten University of Applied Sciences, Campus-Platz 1, 3100 St. Poelten, Austria
| | - Magdalena Führer
- Department of Health and Social Sciences, St. Poelten University of Applied Sciences, Campus-Platz 1, 3100 St. Poelten, Austria
| | - Robert Emprechtinger
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
| | - Dietmar Dammerer
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Richard Ljuhar
- ImageBiopsy Lab GmbH, Zehetnergasse 6/2/2, 1140 Vienna, Austria
| | | | - Stefan Nehrer
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
- Correspondence:
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Yang J, Ji Q, Ni M, Zhang G, Wang Y. Automatic assessment of knee osteoarthritis severity in portable devices based on deep learning. J Orthop Surg Res 2022; 17:540. [PMID: 36514158 PMCID: PMC9749242 DOI: 10.1186/s13018-022-03429-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 12/03/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND For knee osteoarthritis, the commonly used radiology severity criteria Kellgren-Lawrence lead to variability among surgeons. Most existing diagnosis models require preprocessed radiographs and specific equipment. METHODS All enrolled patients diagnosed with KOA who met the criteria were obtained from **** Hospital. This study included 2579 images shot from posterior-anterior X-rays of 2,378 patients. We used RefineDet to train and validate this deep learning-based diagnostic model. After developing the model, 823 images of 697 patients were enrolled as the test set. The whole test set was assessed by up to 5 surgeons and this diagnostic model. To evaluate the model's performance we compared the results of the model with the KOA severity diagnoses of surgeons based on K-L scales. RESULTS Compared to the diagnoses of surgeons, the model achieved an overall accuracy of 0.977. Its sensitivity (recall) for K-L 0 to 4 was 1.0, 0.972, 0.979, 0.983 and 0.989, respectively; for these diagnoses, the specificity of this model was 0.992, 0.997, 0.994, 0.991 and 0.995. The precision and F1-score were 0.5 and 0.667 for K-L 0, 0.914 and 0.930 for K-L 1, 0.978 and 0.971 for K-L 2, 0.981 and 0.974 for K-L 3, and 0.988 and 0.985 for K-L 4, respectively. All K-L scales perform AUC > 0.90. The quadratic weighted Kappa coefficient between the diagnostic model and surgeons was 0.815 (P < 0.01, 95% CI 0.727-0.903). The performance of the model is comparable to the clinical diagnosis of KOA. This model improved the efficiency and avoided cumbersome image preprocessing. CONCLUSION The deep learning-based diagnostic model can be used to assess the severity of KOA in portable devices according to the Kellgren-Lawrence scale. On the premise of improving diagnostic efficiency, the results are highly reliable and reproducible.
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Affiliation(s)
- Jianfeng Yang
- grid.488137.10000 0001 2267 2324Medical School of Chinese PLA, Beijing, 100853 China ,grid.414252.40000 0004 1761 8894Department of Orthopedics, The First Medical Center, Chinese People’s Liberation Army General Hospital, Fuxing Road, Haidian District, Beijing, 100048 China ,grid.414252.40000 0004 1761 8894Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, 100048 China
| | - Quanbo Ji
- grid.414252.40000 0004 1761 8894Department of Orthopedics, The First Medical Center, Chinese People’s Liberation Army General Hospital, Fuxing Road, Haidian District, Beijing, 100048 China ,grid.414252.40000 0004 1761 8894Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, 100048 China
| | - Ming Ni
- grid.414252.40000 0004 1761 8894Department of Orthopedics, The First Medical Center, Chinese People’s Liberation Army General Hospital, Fuxing Road, Haidian District, Beijing, 100048 China ,grid.414252.40000 0004 1761 8894Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, 100048 China
| | - Guoqiang Zhang
- grid.414252.40000 0004 1761 8894Department of Orthopedics, The First Medical Center, Chinese People’s Liberation Army General Hospital, Fuxing Road, Haidian District, Beijing, 100048 China ,grid.414252.40000 0004 1761 8894Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, 100048 China
| | - Yan Wang
- grid.414252.40000 0004 1761 8894Department of Orthopedics, The First Medical Center, Chinese People’s Liberation Army General Hospital, Fuxing Road, Haidian District, Beijing, 100048 China ,grid.414252.40000 0004 1761 8894Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, 100048 China
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Ciliberti FK, Cesarelli G, Guerrini L, Gunnarsson AE, Forni R, Aubonnet R, Recenti M, Jacob D, Jónsson H, Cangiano V, Islind AS, Gambacorta M, Gargiulo P. The role of bone mineral density and cartilage volume to predict knee cartilage degeneration. Eur J Transl Myol 2022; 32. [PMID: 35766481 PMCID: PMC9295173 DOI: 10.4081/ejtm.2022.10678] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 06/22/2022] [Indexed: 12/02/2022] Open
Abstract
Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features’ trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.
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Affiliation(s)
| | - Giuseppe Cesarelli
- Department of Chemical, Materials and Production Engineering (DICMaPI), University of Naples Federico II, Naples.
| | - Lorena Guerrini
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
| | | | - Riccardo Forni
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland; Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Cesena.
| | - Romain Aubonnet
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
| | - Marco Recenti
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
| | - Deborah Jacob
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
| | - Halldór Jónsson
- Department of Orthopaedics, Landspitali, University Hospital of Iceland, Reykjavik, Iceland; Medical Faculty, University of Iceland, Reykjavik.
| | - Vincenzo Cangiano
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
| | | | | | - Paolo Gargiulo
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland; Department of Science, Landspitali, University Hospital of Iceland, Reykjavik.
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