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Liu Y, Yibulayimu S, Zhu G, Shi C, Liang C, Zhao C, Wu X, Sang Y, Wang Y. Automatic pelvic fracture segmentation: a deep learning approach and benchmark dataset. Front Med (Lausanne) 2025; 12:1511487. [PMID: 40303367 PMCID: PMC12039937 DOI: 10.3389/fmed.2025.1511487] [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: 10/15/2024] [Accepted: 03/28/2025] [Indexed: 05/02/2025] Open
Abstract
Introduction Accurate segmentation of pelvic fractures from computed tomography (CT) is crucial for trauma diagnosis and image-guided reduction surgery. The traditional manual slice-by-slice segmentation by surgeons is time-consuming, experience-dependent, and error-prone. The complex anatomy of the pelvic bone, the diversity of fracture types, and the variability in fracture surface appearances pose significant challenges to automated solutions. Methods We propose an automatic pelvic fracture segmentation method based on deep learning, which effectively isolates hipbone and sacrum fragments from fractured pelvic CT. The method employs two sequential networks: an anatomical segmentation network for extracting hipbones and sacrum from CT images, followed by a fracture segmentation network that isolates the main and minor fragments within each bone region. We propose a distance-weighted loss to guide the fracture segmentation network's attention on the fracture surface. Additionally, multi-scale deep supervision and smooth transition strategies are incorporated to enhance overall performance. Results Tested on a curated dataset of 150 CTs, which we have made publicly available, our method achieves an average Dice coefficient of 0.986 and an average symmetric surface distance of 0.234 mm. Discussion The method outperformed traditional max-flow and a transformer-based method, demonstrating its effectiveness in handling complex fracture.
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Affiliation(s)
- Yanzhen Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Sutuke Yibulayimu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Gang Zhu
- Beijing Rossum Robot Technology Co., Ltd., Beijing, China
| | - Chao Shi
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Chendi Liang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Chunpeng Zhao
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Beijing, China
| | - Xinbao Wu
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Beijing, China
| | - Yudi Sang
- Beijing Rossum Robot Technology Co., Ltd., Beijing, China
| | - Yu Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Beijing Rossum Robot Technology Co., Ltd., Beijing, China
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Longo UG, Lalli A, Nicodemi G, Pisani MG, De Sire A, D'Hooghe P, Nazarian A, Oeding JF, Zsidai B, Samuelsson K. Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review. J Exp Orthop 2025; 12:e70259. [PMID: 40337671 PMCID: PMC12056712 DOI: 10.1002/jeo2.70259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Revised: 12/05/2024] [Accepted: 12/17/2024] [Indexed: 05/09/2025] Open
Abstract
Purpose While several artificial intelligence (AI)-assisted medical imaging applications are reported in the recent orthopaedic literature, comparison of the clinical efficacy and utility of these applications is currently lacking. The aim of this systematic review is to evaluate the effectiveness and reliability of AI applications in orthopaedic imaging, focusing on their impact on diagnostic accuracy, image segmentation and operational efficiency across various imaging modalities. Methods Based on the PRISMA guidelines, a comprehensive literature search of PubMed, Cochrane and Scopus databases was performed, using combinations of keywords and MeSH descriptors ('AI', 'ML', 'deep learning', 'orthopaedic surgery' and 'imaging') from inception to March 2024. Included were studies published between September 2018 and February 2024, which evaluated machine learning (ML) model effectiveness in improving orthopaedic imaging. Studies with insufficient data regarding the output variable used to assess the reliability of the ML model, those applying deterministic algorithms, unrelated topics, protocol studies, and other systematic reviews were excluded from the final synthesis. The Joanna Briggs Institute (JBI) Critical Appraisal tool and the Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) tool were applied for the assessment of bias among the included studies. Results The 53 included studies reported the use of 11.990.643 images from several diagnostic instruments. A total of 39 studies reported details in terms of the Dice Similarity Coefficient (DSC), while both accuracy and sensitivity were documented across 15 studies. Precision was reported by 14, specificity by nine, and the F1 score by four of the included studies. Three studies applied the area under the curve (AUC) method to evaluate ML model performance. Among the studies included in the final synthesis, Convolutional Neural Networks (CNN) emerged as the most frequently applied category of ML models, present in 17 studies (32%). Conclusion The systematic review highlights the diverse application of AI in orthopaedic imaging, demonstrating the capability of various machine learning models in accurately segmenting and analysing orthopaedic images. The results indicate that AI models achieve high performance metrics across different imaging modalities. However, the current body of literature lacks comprehensive statistical analysis and randomized controlled trials, underscoring the need for further research to validate these findings in clinical settings. Level of evidence Systematic Review; Level of evidence IV.
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Affiliation(s)
- Umile Giuseppe Longo
- Fondazione Policlinico Universitario Campus Bio‐MedicoRomaItaly
- Department of Medicine and SurgeryResearch Unit of Orthopaedic and Trauma Surgery, Università Campus Bio‐Medico di RomaRomaItaly
| | - Alberto Lalli
- Fondazione Policlinico Universitario Campus Bio‐MedicoRomaItaly
- Department of Medicine and SurgeryResearch Unit of Orthopaedic and Trauma Surgery, Università Campus Bio‐Medico di RomaRomaItaly
| | - Guido Nicodemi
- Fondazione Policlinico Universitario Campus Bio‐MedicoRomaItaly
- Department of Medicine and SurgeryResearch Unit of Orthopaedic and Trauma Surgery, Università Campus Bio‐Medico di RomaRomaItaly
| | - Matteo Giuseppe Pisani
- Fondazione Policlinico Universitario Campus Bio‐MedicoRomaItaly
- Department of Medicine and SurgeryResearch Unit of Orthopaedic and Trauma Surgery, Università Campus Bio‐Medico di RomaRomaItaly
| | - Alessandro De Sire
- Department of Medical and Surgical SciencesUniversity of Catanzaro “Magna Grecia”CatanzaroItaly
| | - Pieter D'Hooghe
- Department of Orthopaedic Surgery and SportsmedicineAspetar HospitalDohaQatar
| | - Ara Nazarian
- Carl J. Shapiro Department of Orthopaedic SurgeryMusculoskeletal Translational Innovation Initiative, Beth Israel Deaconess Medical Center, Harvard Medical SchoolBostonMassachusettsUSA
| | - Jacob F. Oeding
- School of Medicine, Mayo Clinic Alix School of MedicineRochesterMinnesotaUSA
- Department of OrthopaedicsInstitute of Clinical Sciences, The Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Balint Zsidai
- Department of OrthopaedicsInstitute of Clinical Sciences, The Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Kristian Samuelsson
- Department of OrthopaedicsInstitute of Clinical Sciences, The Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
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Gatineau G, Shevroja E, Vendrami C, Gonzalez-Rodriguez E, Leslie WD, Lamy O, Hans D. Development and reporting of artificial intelligence in osteoporosis management. J Bone Miner Res 2024; 39:1553-1573. [PMID: 39163489 PMCID: PMC11523092 DOI: 10.1093/jbmr/zjae131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 07/17/2024] [Accepted: 08/01/2024] [Indexed: 08/22/2024]
Abstract
An abundance of medical data and enhanced computational power have led to a surge in artificial intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17, 2020 to February 1, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the minimum information about clinical artificial intelligence modeling checklist. The systematic search yielded 97 articles that fell into 5 areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles), and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7-11) for bone properties assessment, 7.8 (range: 5-11) for osteoporosis classification, 8.4 (range: 7-11) for fracture detection, 7.6 (range: 4-11) for risk prediction, and 9.0 (range: 6-11) for bone segmentation. A sixth area, AI-driven clinical decision support, identified the studies from the 5 preceding areas that aimed to improve clinician efficiency, diagnostic accuracy, and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision-making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes.
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Affiliation(s)
- Guillaume Gatineau
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland
| | - Enisa Shevroja
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland
| | - Colin Vendrami
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland
| | - Elena Gonzalez-Rodriguez
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland
| | - William D Leslie
- Department of Medicine, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Olivier Lamy
- Internal Medicine Unit, Internal Medicine Department, Lausanne University Hospital and University of Lausanne, 1005 Lausanne, Switzerland
| | - Didier Hans
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland
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Aldieri A, Biondi R, La Mattina AA, Szyszko JA, Polizzi S, Dall'Olio D, Curti N, Castellani G, Viceconti M. Development and validation of a semi-automated and unsupervised method for femur segmentation from CT. Sci Rep 2024; 14:7403. [PMID: 38548805 PMCID: PMC10978861 DOI: 10.1038/s41598-024-57618-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/20/2024] [Indexed: 04/01/2024] Open
Abstract
Quantitative computed tomography (QCT)-based in silico models have demonstrated improved accuracy in predicting hip fractures with respect to the current gold standard, the areal bone mineral density. These models require that the femur bone is segmented as a first step. This task can be challenging, and in fact, it is often almost fully manual, which is time-consuming, operator-dependent, and hard to reproduce. This work proposes a semi-automated procedure for femur bone segmentation from CT images. The proposed procedure is based on the bone and joint enhancement filter and graph-cut algorithms. The semi-automated procedure performances were assessed on 10 subjects through comparison with the standard manual segmentation. Metrics based on the femur geometries and the risk of fracture assessed in silico resulting from the two segmentation procedures were considered. The average Hausdorff distance (0.03 ± 0.01 mm) and the difference union ratio (0.06 ± 0.02) metrics computed between the manual and semi-automated segmentations were significantly higher than those computed within the manual segmentations (0.01 ± 0.01 mm and 0.03 ± 0.02). Besides, a blind qualitative evaluation revealed that the semi-automated procedure was significantly superior (p < 0.001) to the manual one in terms of fidelity to the CT. As for the hip fracture risk assessed in silico starting from both segmentations, no significant difference emerged between the two (R2 = 0.99). The proposed semi-automated segmentation procedure overcomes the manual one, shortening the segmentation time and providing a better segmentation. The method could be employed within CT-based in silico methodologies and to segment large volumes of images to train and test fully automated and supervised segmentation methods.
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Affiliation(s)
- Alessandra Aldieri
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | - Riccardo Biondi
- IRCCS Bologna - Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Antonino A La Mattina
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Julia A Szyszko
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Stefano Polizzi
- Department of Medical and Surgical Sciences, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Daniele Dall'Olio
- IRCCS Bologna - Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Nico Curti
- IRCCS Bologna - Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Physics and Astronomy, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Gastone Castellani
- Department of Medical and Surgical Sciences, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Marco Viceconti
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy
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