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Malekipour F, Whitton RC, Lee PVS. Advancements in Subchondral Bone Biomechanics: Insights from Computed Tomography and Micro-Computed Tomography Imaging in Equine Models. Curr Osteoporos Rep 2024:10.1007/s11914-024-00886-y. [PMID: 39276168 DOI: 10.1007/s11914-024-00886-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/30/2024] [Indexed: 09/16/2024]
Abstract
PURPOSE OF REVIEW This review synthesizes recent advancements in understanding subchondral bone (SCB) biomechanics using computed tomography (CT) and micro-computed tomography (micro-CT) imaging in large animal models, particularly horses. RECENT FINDINGS Recent studies highlight the complexity of SCB biomechanics, revealing variability in density, microstructure, and biomechanical properties across the depth of SCB from the joint surface, as well as at different joint locations. Early SCB abnormalities have been identified as predictive markers for both osteoarthritis (OA) and stress fractures. The development of standing CT systems has improved the practicality and accuracy of live animal imaging, aiding early diagnosis of SCB pathologies. While imaging advancements have enhanced our understanding of SCB, further research is required to elucidate the underlying mechanisms of joint disease and articular surface failure. Combining imaging with mechanical testing, computational modelling, and artificial intelligence (AI) promises earlier detection and better management of joint disease. Future research should refine these modalities and integrate them into clinical practice to enhance joint health outcomes in veterinary and human medicine.
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Affiliation(s)
- Fatemeh Malekipour
- Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia.
| | - R Chris Whitton
- Equine Centre, Department of Veterinary Clinical Sciences, University of Melbourne, Werribee, VIC, 3030, Australia
| | - Peter Vee-Sin Lee
- Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia
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Nudelman H, Lőrincz A, Kassai T, Józsa G. Juvenile Osteochondritis Dissecans: A Case Report. Diagnostics (Basel) 2024; 14:1931. [PMID: 39272716 PMCID: PMC11394152 DOI: 10.3390/diagnostics14171931] [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: 08/01/2024] [Revised: 08/22/2024] [Accepted: 08/30/2024] [Indexed: 09/15/2024] Open
Abstract
(1) Background: This report aims to illustrate the development, progression, diagnosis, and treatment of chronically present articular surface lesions. (2) Methods: In this report, two patients are described from the point of the initial presentation of symptoms to surgical consultation based on radiologic findings. These patients underwent corrective surgery in the form of mosaicplasty to repair lesions present on the articular surface and the underlying subchondral bone. (3) Discussion: Diagnosing juvenile OCD remains challenging due to its variable clinical presentation and minute radiologic discoveries. X-rays are useful; however, the gold standard remains arthroscopy, which can be both diagnostic and therapeutic. Future prospects include the use of novel sonographic methods and the use of artificial intelligence within the given modalities. (4) Conclusions: The detailed imaging provided by MRI, combined with the insights from X-rays and potentially other modalities, allows for a nuanced understanding of this disease. This comprehensive approach ensures that treatment decisions are well-informed, optimising outcomes for young patients with this condition.
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Affiliation(s)
- Hermann Nudelman
- Department of Thermophysiology, Institute for Translational Medicine, Medical School, University of Pécs, 12 Szigeti Street, 7624 Pécs, Hungary
- Division of Pediatric Surgery, Traumatology, Urology and Pediatric Otolaryngology, Department of Pediatrics, Medical School, University of Pécs, 7 József Attila Street, 7623 Pécs, Hungary
| | - Aba Lőrincz
- Department of Thermophysiology, Institute for Translational Medicine, Medical School, University of Pécs, 12 Szigeti Street, 7624 Pécs, Hungary
- Division of Pediatric Surgery, Traumatology, Urology and Pediatric Otolaryngology, Department of Pediatrics, Medical School, University of Pécs, 7 József Attila Street, 7623 Pécs, Hungary
| | - Tamás Kassai
- Department of Pediatric Traumatology, Péterfy Hospital, Manninger Jenő National Trauma Center, 17 Fiumei Street, 1081 Budapest, Hungary
| | - Gergő Józsa
- Department of Thermophysiology, Institute for Translational Medicine, Medical School, University of Pécs, 12 Szigeti Street, 7624 Pécs, Hungary
- Division of Pediatric Surgery, Traumatology, Urology and Pediatric Otolaryngology, Department of Pediatrics, Medical School, University of Pécs, 7 József Attila Street, 7623 Pécs, Hungary
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Su JH, Tung YC, Liao YW, Wang HY, Chen BH, Chang CD, Cheng YF, Chang WC, Chin CY. Deep Learning-Based Surgical Treatment Recommendation and Nonsurgical Prognosis Status Classification for Scaphoid Fractures by Automated X-ray Image Recognition. Biomedicines 2024; 12:1198. [PMID: 38927405 PMCID: PMC11201164 DOI: 10.3390/biomedicines12061198] [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: 04/01/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024] Open
Abstract
Biomedical information retrieval for diagnosis, treatment and prognosis has been studied for a long time. In particular, image recognition using deep learning has been shown to be very effective for cancers and diseases. In these fields, scaphoid fracture recognition is a hot topic because the appearance of scaphoid fractures is not easy to detect. Although there have been a number of recent studies on this topic, no studies focused their attention on surgical treatment recommendations and nonsurgical prognosis status classification. Indeed, a successful treatment recommendation will assist the doctor in selecting an effective treatment, and the prognosis status classification will help a radiologist recognize the image more efficiently. For these purposes, in this paper, we propose potential solutions through a comprehensive empirical study assessing the effectiveness of recent deep learning techniques on surgical treatment recommendation and nonsurgical prognosis status classification. In the proposed system, the scaphoid is firstly segmented from an unknown X-ray image. Next, for surgical treatment recommendation, the fractures are further filtered and recognized. According to the recognition result, the surgical treatment recommendation is generated. Finally, even without sufficient fracture information, the doctor can still make an effective decision to opt for surgery or not. Moreover, for nonsurgical patients, the current prognosis status of avascular necrosis, non-union and union can be classified. The related experimental results made using a real dataset reveal that the surgical treatment recommendation reached 80% and 86% in accuracy and AUC (Area Under the Curve), respectively, while the nonsurgical prognosis status classification reached 91% and 96%, respectively. Further, the methods using transfer learning and data augmentation can bring out obvious improvements, which, on average, reached 21.9%, 28.9% and 5.6%, 7.8% for surgical treatment recommendations and nonsurgical prognosis image classification, respectively. Based on the experimental results, the recommended methods in this paper are DenseNet169 and ResNet50 for surgical treatment recommendation and nonsurgical prognosis status classification, respectively. We believe that this paper can provide an important reference for future research on surgical treatment recommendation and nonsurgical prognosis classification for scaphoid fractures.
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Affiliation(s)
- Ja-Hwung Su
- Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan;
| | - Yu-Cheng Tung
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (C.-D.C.); (Y.-F.C.); (W.-C.C.)
| | - Yi-Wen Liao
- Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 82444, Taiwan; (Y.-W.L.); (H.-Y.W.)
| | - Hung-Yu Wang
- Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 82444, Taiwan; (Y.-W.L.); (H.-Y.W.)
| | - Bo-Hong Chen
- Department of Information Management, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan;
| | - Ching-Di Chang
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (C.-D.C.); (Y.-F.C.); (W.-C.C.)
| | - Yu-Fan Cheng
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (C.-D.C.); (Y.-F.C.); (W.-C.C.)
| | - Wan-Ching Chang
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (C.-D.C.); (Y.-F.C.); (W.-C.C.)
| | - Chu-Yu Chin
- Telecommunication Laboratories Chunghwa Telecom Company Limited, Kaohsiung 80002, Taiwan;
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Lee CW, Huang CC, Jang YC, Chen KC, Ho SY, Chou CT, Wu WP. Diagnostic Accuracy for Acute Rib Fractures: A Cross-sectional Study Utilizing Automatic Rib Unfolding and 3D Volume-Rendered Reformation. Acad Radiol 2024; 31:1538-1547. [PMID: 37845164 DOI: 10.1016/j.acra.2023.08.037] [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: 05/20/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 10/18/2023]
Abstract
RATIONALE AND OBJECTIVES The aim of this study was to compare the use of computed tomography (CT) with automatic rib unfolding and three-dimensional (3D) volume-rendered imaging in the detection and characterization of rib fractures and flail chest. MATERIALS AND METHODS A total of 130 patients with blunt chest trauma underwent whole-body CT, and five independent readers assessed the presence and characterization of rib fractures using traditional CT images, automatic rib unfolding, and 3D volume-rendered images in separate readout sessions at least 2 weeks apart. A gold standard was established by consensus among the readers based on the combined analysis of conventional and reformatted images. RESULTS Automatic rib unfolding significantly reduced mean reading time by 47.5%-74.9% (P < 0.0001) while maintaining a comparable diagnostic performance for rib fractures (positive predictive value [PPV] of 82.1%-93.5%, negative predictive value [NPV] of 96.8%-98.2%, and 69.4%-94.2% and 96.9%-99.1% for conventional axial images and 70.4%-85.1% and 95.2%-96.6% for 3D images) and better interobserver agreement (kappa of 0.74-0.87). For flail chest, automatic rib unfolding showed a PPV of 85.7%-100%, NPV of 90.4%-99.0%, and 80.0%-100% and 89.7%-100% for conventional axial images and 76.9%-100% and 89.0%-92.1% for 3D images. CONCLUSION Automatic rib unfolding demonstrated equivalent diagnostic performance to conventional images in detecting acute rib fractures and flail chest, with good interobserver agreement and time-saving benefits.
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Affiliation(s)
- Chih-Wei Lee
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.)
| | - Cheng-Chieh Huang
- Department of Emergency and Critical Care Medicine, Changhua Christian Hospital, Changhua, Taiwan (C.-C.H., K.-C.C.); Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan (C.-C.H.)
| | - Yong-Ching Jang
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.)
| | - Kuan-Chih Chen
- Department of Emergency and Critical Care Medicine, Changhua Christian Hospital, Changhua, Taiwan (C.-C.H., K.-C.C.)
| | - Shang-Yun Ho
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.); Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan (S.-Y.H.)
| | - Chen-Te Chou
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.); Kaohsiung Medical University, Kaohsiung, Taiwan (C.-T.C., W.-P.W)
| | - Wen-Pei Wu
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.); Kaohsiung Medical University, Kaohsiung, Taiwan (C.-T.C., W.-P.W); Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan (W.-P.W.).
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Hakam HT, Prill R, Korte L, Lovreković B, Ostojić M, Ramadanov N, Muehlensiepen F. Human-Written vs AI-Generated Texts in Orthopedic Academic Literature: Comparative Qualitative Analysis. JMIR Form Res 2024; 8:e52164. [PMID: 38363631 PMCID: PMC10907945 DOI: 10.2196/52164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/09/2023] [Accepted: 12/13/2023] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND As large language models (LLMs) are becoming increasingly integrated into different aspects of health care, questions about the implications for medical academic literature have begun to emerge. Key aspects such as authenticity in academic writing are at stake with artificial intelligence (AI) generating highly linguistically accurate and grammatically sound texts. OBJECTIVE The objective of this study is to compare human-written with AI-generated scientific literature in orthopedics and sports medicine. METHODS Five original abstracts were selected from the PubMed database. These abstracts were subsequently rewritten with the assistance of 2 LLMs with different degrees of proficiency. Subsequently, researchers with varying degrees of expertise and with different areas of specialization were asked to rank the abstracts according to linguistic and methodological parameters. Finally, researchers had to classify the articles as AI generated or human written. RESULTS Neither the researchers nor the AI-detection software could successfully identify the AI-generated texts. Furthermore, the criteria previously suggested in the literature did not correlate with whether the researchers deemed a text to be AI generated or whether they judged the article correctly based on these parameters. CONCLUSIONS The primary finding of this study was that researchers were unable to distinguish between LLM-generated and human-written texts. However, due to the small sample size, it is not possible to generalize the results of this study. As is the case with any tool used in academic research, the potential to cause harm can be mitigated by relying on the transparency and integrity of the researchers. With scientific integrity at stake, further research with a similar study design should be conducted to determine the magnitude of this issue.
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Affiliation(s)
- Hassan Tarek Hakam
- Center of Orthopaedics and Trauma Surgery, University Clinic of Brandenburg, Brandenburg Medical School, Brandenburg an der Havel, Germany
- Faculty of Health Sciences, University Clinic of Brandenburg, Brandenburg an der Havel, Germany
- Center of Evidence Based Practice in Brandenburg, a JBI Affiliated Group, Brandenburg an der Havel, Germany
| | - Robert Prill
- Faculty of Health Sciences, University Clinic of Brandenburg, Brandenburg an der Havel, Germany
- Center of Evidence Based Practice in Brandenburg, a JBI Affiliated Group, Brandenburg an der Havel, Germany
| | - Lisa Korte
- Center of Health Services Research, Faculty of Health Sciences, University Clinic of Brandenburg, Rüdersdorf bei Berlin, Germany
| | - Bruno Lovreković
- Faculty of Orthopaedics, University Hospital Merkur, Zagreb, Croatia
| | - Marko Ostojić
- Departement of Orthopaedics, University Hospital Mostar, Mostar, Bosnia and Herzegovina
| | - Nikolai Ramadanov
- Center of Orthopaedics and Trauma Surgery, University Clinic of Brandenburg, Brandenburg Medical School, Brandenburg an der Havel, Germany
- Faculty of Health Sciences, University Clinic of Brandenburg, Brandenburg an der Havel, Germany
| | - Felix Muehlensiepen
- Center of Evidence Based Practice in Brandenburg, a JBI Affiliated Group, Brandenburg an der Havel, Germany
- Center of Health Services Research, Faculty of Health Sciences, University Clinic of Brandenburg, Rüdersdorf bei Berlin, Germany
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Pham TD, Holmes SB, Coulthard P. A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging. Front Artif Intell 2024; 6:1278529. [PMID: 38249794 PMCID: PMC10797131 DOI: 10.3389/frai.2023.1278529] [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: 08/16/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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