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Oeding JF, Kunze KN, Messer CJ, Pareek A, Fufa DT, Pulos N, Rhee PC. Diagnostic Performance of Artificial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review. J Hand Surg Am 2024; 49:411-422. [PMID: 38551529 DOI: 10.1016/j.jhsa.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 01/19/2024] [Accepted: 01/31/2024] [Indexed: 05/05/2024]
Abstract
PURPOSE To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts. METHODS PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded. RESULTS A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans. CONCLUSIONS AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance. CLINICAL RELEVANCE AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.
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Affiliation(s)
- Jacob F Oeding
- School of Medicine, Mayo Clinic Alix School of Medicine, Rochester, MN; Department of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy, University of Gotenburg, Gothenburg, Sweden.
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Caden J Messer
- School of Medicine, Mayo Clinic Alix School of Medicine, Rochester, MN
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Duretti T Fufa
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Nicholas Pulos
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN
| | - Peter C Rhee
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN
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Hansen V, Jensen J, Kusk MW, Gerke O, Tromborg HB, Lysdahlgaard S. Deep learning performance compared to healthcare experts in detecting wrist fractures from radiographs: A systematic review and meta-analysis. Eur J Radiol 2024; 174:111399. [PMID: 38428318 DOI: 10.1016/j.ejrad.2024.111399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/29/2024] [Accepted: 02/26/2024] [Indexed: 03/03/2024]
Abstract
OBJECTIVE To perform a systematic review and meta-analysis of the diagnostic accuracy of deep learning (DL) algorithms in the diagnosis of wrist fractures (WF) on plain wrist radiographs, taking healthcare experts consensus as reference standard. METHODS Embase, Medline, PubMed, Scopus and Web of Science were searched in the period from 1 Jan 2012 to 9 March 2023. Eligible studies were patients with wrist radiographs for radial and ulnar fractures as the target condition, studies using DL algorithms based on convolutional neural networks (CNN), and healthcare experts consensus as the minimum reference standard. Studies were assessed with a modified QUADAS-2 tool, and we applied a bivariate random-effects model for meta-analysis of diagnostic test accuracy data. RESULTS Our study was registered at PROSPERO with ID: CRD42023431398. We included 6 unique studies for meta-analysis, with a total of 33,026 radiographs. CNN performance compared to reference standards for the included articles found a summary sensitivity of 92% (95% CI: 80%-97%) and a summary specificity of 93% (95% CI: 76%-98%). The generalized bivariate I-squared statistic indicated considerable heterogeneity between the studies (81.90%). Four studies had one or more domains at high risk of bias and two studies had concerns regarding applicability. CONCLUSION The diagnostic accuracy of CNNs was comparable to that of healthcare experts in wrist radiographs for investigation of WF. There is a need for studies with a robust reference standard, external data-set validation and investigation of diagnostic performance of healthcare experts aided with CNNs. CLINICAL RELEVANCE STATEMENT DL matches healthcare experts in diagnosing WFs, which potentially benefits patient diagnosis.
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Affiliation(s)
- V Hansen
- Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark
| | - J Jensen
- Department of Radiology, Odense University Hospital, Odense, Denmark; Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark
| | - M W Kusk
- Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark; Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Imaging Research Initiative Southwest (IRIS), Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark; Radiography and Diagnostic Imaging, School of Medicine, University College Dublin, Belfield 4, Dublin, Ireland
| | - O Gerke
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - H B Tromborg
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Department of Orthopedic Surgery, Odense University Hospital, Odense, Denmark
| | - S Lysdahlgaard
- Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark; Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Imaging Research Initiative Southwest (IRIS), Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark.
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Mert S, Stoerzer P, Brauer J, Fuchs B, Haas-Lützenberger EM, Demmer W, Giunta RE, Nuernberger T. Diagnostic power of ChatGPT 4 in distal radius fracture detection through wrist radiographs. Arch Orthop Trauma Surg 2024:10.1007/s00402-024-05298-2. [PMID: 38578309 DOI: 10.1007/s00402-024-05298-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 03/27/2024] [Indexed: 04/06/2024]
Abstract
Distal radius fractures rank among the most prevalent fractures in humans, necessitating accurate radiological imaging and interpretation for optimal diagnosis and treatment. In addition to human radiologists, artificial intelligence systems are increasingly employed for radiological assessments. Since 2023, ChatGPT 4 has offered image analysis capabilities, which can also be used for the analysis of wrist radiographs. This study evaluates the diagnostic power of ChatGPT 4 in identifying distal radius fractures, comparing it with a board-certified radiologist, a hand surgery resident, a medical student, and the well-established AI Gleamer BoneView™. Results demonstrate ChatGPT 4's good diagnostic accuracy (sensitivity 0.88, specificity 0.98, diagnostic power (AUC) 0.93), surpassing the medical student (sensitivity 0.98, specificity 0.72, diagnostic power (AUC) 0.85; p = 0.04) significantly. Nevertheless, the diagnostic power of ChatGPT 4 lags behind the hand surgery resident (sensitivity 0.99, specificity 0.98, diagnostic power (AUC) 0.985; p = 0.014) and Gleamer BoneView™(sensitivity 1.00, specificity 0.98, diagnostic power (AUC) 0.99; p = 0.006). This study highlights the utility and potential applications of artificial intelligence in modern medicine, emphasizing ChatGPT 4 as a valuable tool for enhancing diagnostic capabilities in the field of medical imaging.
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Affiliation(s)
- Sinan Mert
- Division of Hand, Plastic and Aesthetic Surgery, LMU University Hospital, LMU Munich, 80336, München, Germany.
| | - Patrick Stoerzer
- Division of Hand, Plastic and Aesthetic Surgery, LMU University Hospital, LMU Munich, 80336, München, Germany
| | - Johannes Brauer
- Division of Hand, Plastic and Aesthetic Surgery, LMU University Hospital, LMU Munich, 80336, München, Germany
| | - Benedikt Fuchs
- Division of Hand, Plastic and Aesthetic Surgery, LMU University Hospital, LMU Munich, 80336, München, Germany
| | | | - Wolfram Demmer
- Division of Hand, Plastic and Aesthetic Surgery, LMU University Hospital, LMU Munich, 80336, München, Germany
| | - Riccardo E Giunta
- Division of Hand, Plastic and Aesthetic Surgery, LMU University Hospital, LMU Munich, 80336, München, Germany
| | - Tim Nuernberger
- Division of Hand, Plastic and Aesthetic Surgery, LMU University Hospital, LMU Munich, 80336, München, Germany
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Jung J, Dai J, Liu B, Wu Q. Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis. PLOS Digit Health 2024; 3:e0000438. [PMID: 38289965 PMCID: PMC10826962 DOI: 10.1371/journal.pdig.0000438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 12/25/2023] [Indexed: 02/01/2024]
Abstract
Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87-96, p< 0.01) and specificity (90%; 95% CI: 85-93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90-94, p< 0.01; and 91%; 95% CI: 88-93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77-85, p< 0.01; and 83%; 95% CI: 76-88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90-96, p < 0.01) and specificity (92%; 95% CI: 89-94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).
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Affiliation(s)
- Jongyun Jung
- Department of Biomedical Informatics (Dr. Qing Wu, Jongyun Jung, and Jingyuan Dai), College of Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Jingyuan Dai
- Department of Biomedical Informatics (Dr. Qing Wu, Jongyun Jung, and Jingyuan Dai), College of Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Bowen Liu
- Department of Mathematics and Statistics, Division of Computing, Analytics, and Mathematics, School of Science and Engineering (Bowen Liu), University of Missouri-Kansas City, Kansas City, Missouri, United States of America
| | - Qing Wu
- Department of Biomedical Informatics (Dr. Qing Wu, Jongyun Jung, and Jingyuan Dai), College of Medicine, The Ohio State University, Columbus, Ohio, United States of America
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Till T, Tschauner S, Singer G, Lichtenegger K, Till H. Development and optimization of AI algorithms for wrist fracture detection in children using a freely available dataset. Front Pediatr 2023; 11:1291804. [PMID: 38188914 PMCID: PMC10768054 DOI: 10.3389/fped.2023.1291804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 12/05/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction In the field of pediatric trauma computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems have emerged offering a promising avenue for improved patient care. Especially children with wrist fractures may benefit from machine learning (ML) solutions, since some of these lesions may be overlooked on conventional X-ray due to minimal compression without dislocation or mistaken for cartilaginous growth plates. In this article, we describe the development and optimization of AI algorithms for wrist fracture detection in children. Methods A team of IT-specialists, pediatric radiologists and pediatric surgeons used the freely available GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, a total number of 10,643 studies (20,327 images). First, a basic object detection model, a You Only Look Once object detector of the seventh generation (YOLOv7) was trained and tested on these data. Then, team decisions were taken to adjust data preparation, image sizes used for training and testing, and configuration of the detection model. Furthermore, we investigated each of these models using an Explainable Artificial Intelligence (XAI) method called Gradient Class Activation Mapping (Grad-CAM). This method visualizes where a model directs its attention to before classifying and regressing a certain class through saliency maps. Results Mean average precision (mAP) improved when applying optimizations pre-processing the dataset images (maximum increases of + 25.51% mAP@0.5 and + 39.78% mAP@[0.5:0.95]), as well as the object detection model itself (maximum increases of + 13.36% mAP@0.5 and + 27.01% mAP@[0.5:0.95]). Generally, when analyzing the resulting models using XAI methods, higher scoring model variations in terms of mAP paid more attention to broader regions of the image, prioritizing detection accuracy over precision compared to the less accurate models. Discussion This paper supports the implementation of ML solutions for pediatric trauma care. Optimization of a large X-ray dataset and the YOLOv7 model improve the model's ability to detect objects and provide valid diagnostic support to health care specialists. Such optimization protocols must be understood and advocated, before comparing ML performances against health care specialists.
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Affiliation(s)
- Tristan Till
- Department of Applied Computer Sciences, FH JOANNEUM - University of Applied Sciences, Graz, Austria
- Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - Sebastian Tschauner
- Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - Georg Singer
- Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria
| | - Klaus Lichtenegger
- Department of Applied Computer Sciences, FH JOANNEUM - University of Applied Sciences, Graz, Austria
| | - Holger Till
- Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria
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Jeon YD, Kang MJ, Kuh SU, Cha HY, Kim MS, You JY, Kim HJ, Shin SH, Chung YG, Yoon DK. Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images. Diagnostics (Basel) 2023; 14:11. [PMID: 38201320 PMCID: PMC10802847 DOI: 10.3390/diagnostics14010011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 11/30/2023] [Accepted: 12/10/2023] [Indexed: 01/12/2024] Open
Abstract
Utilizing "You only look once" (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. The data accepted into the backbone are diversified through CSPDarkNet-53. Feature maps are extracted using Spatial Pyramid Pooling and a Path Aggregation Network in the neck part. The head part aggregates and generates the final output. All bounding boxes by the YOLO v4 are mapped onto the 3D reconstructed bone images after being resized to match the same region as shown in the 2D CT images. The YOLO v4-based AI model was evaluated through precision-recall (PR) curves and the intersection over union (IoU). Our proposed system facilitated an intuitive display of the fractured area through a distinctive red mask overlaid on the 3D reconstructed bone images. The high average precision values (>0.60) were reported as 0.71 and 0.81 from the PR curves of the tibia and elbow, respectively. The IoU values were calculated as 0.6327 (tibia) and 0.6638 (elbow). When utilized by orthopedic surgeons in real clinical scenarios, this AI-powered 3D diagnosis support system could enable a quick and accurate trauma diagnosis.
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Affiliation(s)
- Young-Dae Jeon
- Department of Orthopedic Surgery, University of Ulsan College of Medicine, Ulsan University Hospital, Ulsan 44033, Republic of Korea
| | - Min-Jun Kang
- Department of Integrative Medicine, College of Medicine, Yonsei University of Korea, Seoul 03722, Republic of Korea;
| | - Sung-Uk Kuh
- Department of Integrative Medicine, College of Medicine, Yonsei University of Korea, Seoul 03722, Republic of Korea;
| | - Ha-Yeong Cha
- Industrial R&D Center, KAVILAB Co., Ltd., Seoul 06675, Republic of Korea; (H.-Y.C.); (M.-S.K.); (J.-Y.Y.); (H.-J.K.)
| | - Moo-Sub Kim
- Industrial R&D Center, KAVILAB Co., Ltd., Seoul 06675, Republic of Korea; (H.-Y.C.); (M.-S.K.); (J.-Y.Y.); (H.-J.K.)
| | - Ju-Yeon You
- Industrial R&D Center, KAVILAB Co., Ltd., Seoul 06675, Republic of Korea; (H.-Y.C.); (M.-S.K.); (J.-Y.Y.); (H.-J.K.)
| | - Hyeon-Joo Kim
- Industrial R&D Center, KAVILAB Co., Ltd., Seoul 06675, Republic of Korea; (H.-Y.C.); (M.-S.K.); (J.-Y.Y.); (H.-J.K.)
| | - Seung-Han Shin
- Department of Orthopedic Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; (S.-H.S.); (Y.-G.C.)
| | - Yang-Guk Chung
- Department of Orthopedic Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; (S.-H.S.); (Y.-G.C.)
| | - Do-Kun Yoon
- Industrial R&D Center, KAVILAB Co., Ltd., Seoul 06675, Republic of Korea; (H.-Y.C.); (M.-S.K.); (J.-Y.Y.); (H.-J.K.)
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Chiu SUF, Yang SC, Chiu CC. A commentary on 'Radiographic views for hand fractures - call for three-view national UK guidelines - a quality improvement study'. Int J Surg 2023; 109:4355-4356. [PMID: 37830945 PMCID: PMC10720773 DOI: 10.1097/js9.0000000000000678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 08/04/2023] [Indexed: 10/14/2023]
Affiliation(s)
- Si-Un Frank Chiu
- Department of Computer Science
- Department of Economics, Brown University, Providence, Rhode Island, USA
| | - Shih-Chieh Yang
- Department of Orthopedic Surgery, E-Da Hospital
- School of Medicine, College of Medicine
| | - Chong-Chi Chiu
- School of Medicine, College of Medicine
- Department of General Surgery
- Department of Medical Education and Research, E-Da Cancer Hospital, I-Shou University, Kaohsiung, Taiwan
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Suna A, Davidson A, Weil Y, Joskowicz L. Automated computation of radiographic parameters of distal radial metaphyseal fractures in forearm X-rays. Int J Comput Assist Radiol Surg 2023; 18:2179-2189. [PMID: 37097517 DOI: 10.1007/s11548-023-02907-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 04/03/2023] [Indexed: 04/26/2023]
Abstract
PURPOSE Radiographic parameters (RPs) provide objective support for effective decision making in determining clinical treatment of distal radius fractures (DRFs). This paper presents a novel automatic RP computation pipeline for computing the six anatomical RPs associated with DRFs in anteroposterior (AP) and lateral (LAT) forearm radiographs. METHODS The pipeline consists of: (1) segmentation of the distal radius and ulna bones with six 2D Dynamic U-Net deep learning models; (2) landmark points detection and distal radius axis computation from the segmentations with geometric methods; (3) RP computation and generation of a quantitative DRF report and composite AP and LAT radiograph images. This hybrid approach combines the advantages of deep learning and model-based methods. RESULTS The pipeline was evaluated on 90 AP and 93 LAT radiographs for which ground truth distal radius and ulna segmentations and RP landmarks were manually obtained by expert clinicians. It achieves an accuracy of 94 and 86% on the AP and LAT RPs, within the observer variability, and an RP measurement difference of 1.4 ± 1.2° for the radial angle, 0.5 ± 0.6 mm for the radial length, 0.9 ± 0.7 mm for the radial shift, 0.7 ± 0.5 mm for the ulnar variance, 2.9 ± 3.3° for the palmar tilt and 1.2 ± 1.0 mm for the dorsal shift. CONCLUSION Our pipeline is the first fully automatic method that accurately and robustly computes the RPs for a wide variety of clinical forearm radiographs from different sources, hand orientations, with and without cast. The computed accurate and reliable RF measurements may support fracture severity assessment and clinical management.
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Affiliation(s)
- Avigail Suna
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel, Edmond J. Safra Campus, Givat Ram, 9190401, Jerusalem, Israel
| | - Amit Davidson
- Department of Orthopedics, Hadassah University Medical Center, Jerusalem, Israel
| | - Yoram Weil
- Department of Orthopedics, Hadassah University Medical Center, Jerusalem, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel, Edmond J. Safra Campus, Givat Ram, 9190401, Jerusalem, Israel.
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Fu T, Viswanathan V, Attia A, Zerbib-Attal E, Kosaraju V, Barger R, Vidal J, Bittencourt LK, Faraji N. Assessing the Potential of a Deep Learning Tool to Improve Fracture Detection by Radiologists and Emergency Physicians on Extremity Radiographs. Acad Radiol 2023:S1076-6332(23)00595-0. [PMID: 37993303 DOI: 10.1016/j.acra.2023.10.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/23/2023] [Accepted: 10/25/2023] [Indexed: 11/24/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate the standalone performance of a deep learning (DL) based fracture detection tool on extremity radiographs and assess the performance of radiologists and emergency physicians in identifying fractures of the extremities with and without the DL aid. MATERIALS AND METHODS The DL tool was previously developed using 132,000 appendicular skeletal radiographs divided into 87% training, 11% validation, and 2% test sets. Stand-alone performance was evaluated on 2626 de-identified radiographs from a single institution in Ohio, including at least 140 exams per body region. Consensus from three US board-certified musculoskeletal (MSK) radiologists served as ground truth. A multi-reader retrospective study was performed in which 24 readers (eight each of emergency physicians, non-MSK radiologists, and MSK radiologists) identified fractures in 186 cases during two independent sessions with and without DL aid, separated by a one-month washout period. The accuracy (area under the receiver operating curve), sensitivity, specificity, and reading time were compared with and without model aid. RESULTS The model achieved a stand-alone accuracy of 0.986, sensitivity of 0.987, and specificity of 0.885, and high accuracy (> 0.95) across stratification for body part, age, gender, radiographic views, and scanner type. With DL aid, reader accuracy increased by 0.047 (95% CI: 0.034, 0.061; p = 0.004) and sensitivity significantly improved from 0.865 (95% CI: 0.848, 0.881) to 0.955 (95% CI: 0.944, 0.964). Average reading time was shortened by 7.1 s (27%) per exam. When stratified by physician type, this improvement was greater for emergency physicians and non-MSK radiologists. CONCLUSION The DL tool demonstrated high stand-alone accuracy, aided physician diagnostic accuracy, and decreased interpretation time.
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Affiliation(s)
- Tianyuan Fu
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio, USA (T.F., V.V., V.K., R.B., L.K.B., N.F.).
| | - Vidya Viswanathan
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio, USA (T.F., V.V., V.K., R.B., L.K.B., N.F.)
| | - Alexandre Attia
- Azmed, 10 Rue d'Uzès, 75002, Paris, France (A.A., E.Z.A., J.V.)
| | | | - Vijaya Kosaraju
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio, USA (T.F., V.V., V.K., R.B., L.K.B., N.F.)
| | - Richard Barger
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio, USA (T.F., V.V., V.K., R.B., L.K.B., N.F.)
| | - Julien Vidal
- Azmed, 10 Rue d'Uzès, 75002, Paris, France (A.A., E.Z.A., J.V.)
| | - Leonardo K Bittencourt
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio, USA (T.F., V.V., V.K., R.B., L.K.B., N.F.)
| | - Navid Faraji
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio, USA (T.F., V.V., V.K., R.B., L.K.B., N.F.)
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Jacques T, Cardot N, Ventre J, Demondion X, Cotten A. Commercially-available AI algorithm improves radiologists' sensitivity for wrist and hand fracture detection on X-ray, compared to a CT-based ground truth. Eur Radiol 2023:10.1007/s00330-023-10380-1. [PMID: 37919408 DOI: 10.1007/s00330-023-10380-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 11/04/2023]
Abstract
OBJECTIVES Algorithms for fracture detection are spreading in clinical practice, but the use of X-ray-only ground truth can induce bias in their evaluation. This study assessed radiologists' performances to detect wrist and hand fractures on radiographs, using a commercially-available algorithm, compared to a computerized tomography (CT) ground truth. METHODS Post-traumatic hand and wrist CT and concomitant X-ray examinations were retrospectively gathered. Radiographs were labeled based on CT findings. The dataset was composed of 296 consecutive cases: 118 normal (39.9%), 178 pathological (60.1%) with a total of 267 fractures visible in CT. Twenty-three radiologists with various levels of experience reviewed all radiographs without AI, then using it, blinded towards CT results. RESULTS Using AI improved radiologists' sensitivity (Se, 0.658 to 0.703, p < 0.0001) and negative predictive value (NPV, 0.585 to 0.618, p < 0.0001), without affecting their specificity (Sp, 0.885 vs 0.891, p = 0.91) or positive predictive value (PPV, 0.887 vs 0.899, p = 0.08). On the radiographic dataset, based on the CT ground truth, stand-alone AI performances were 0.771 (Se), 0.898 (Sp), 0.684 (NPV), 0.915 (PPV), and 0.764 (AUROC) which were lower than previously reported, suggesting a potential underestimation of the number of missed fractures in the AI literature. CONCLUSIONS AI enabled radiologists to improve their sensitivity and negative predictive value for wrist and hand fracture detection on radiographs, without affecting their specificity or positive predictive value, compared to a CT-based ground truth. Using CT as gold standard for X-ray labels is innovative, leading to algorithm performance poorer than reported elsewhere, but probably closer to clinical reality. CLINICAL RELEVANCE STATEMENT Using an AI algorithm significantly improved radiologists' sensitivity and negative predictive value in detecting wrist and hand fractures on radiographs, with ground truth labels based on CT findings. KEY POINTS • Using CT as a ground truth for labeling X-rays is new in AI literature, and led to algorithm performance significantly poorer than reported elsewhere (AUROC: 0.764), but probably closer to clinical reality. • AI enabled radiologists to significantly improve their sensitivity (+ 4.5%) and negative predictive value (+ 3.3%) for the detection of wrist and hand fractures on X-rays. • There was no significant change in terms of specificity or positive predictive value.
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Affiliation(s)
- Thibaut Jacques
- Department of Musculoskeletal Radiology, Lille University Hospital, Rue du Professeur Emile Laine, 59000, Lille, France.
- IRIS Radiology - Clinique Lille Sud, SOS Hands and Fingers, 96 Rue Gustave Delory, 59810, Lesquin, France.
| | - Nicolas Cardot
- Department of Musculoskeletal Radiology, Lille University Hospital, Rue du Professeur Emile Laine, 59000, Lille, France
| | | | - Xavier Demondion
- Department of Musculoskeletal Radiology, Lille University Hospital, Rue du Professeur Emile Laine, 59000, Lille, France
- Lille University School of Medicine, 59000, Lille, France
| | - Anne Cotten
- Department of Musculoskeletal Radiology, Lille University Hospital, Rue du Professeur Emile Laine, 59000, Lille, France
- Lille University School of Medicine, 59000, Lille, France
- MABLab - Marrow Adiposity and Bone Lab ULR4490, University of Lille, 59000, Lille, France
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Abedeen I, Rahman MA, Prottyasha FZ, Ahmed T, Chowdhury TM, Shatabda S. FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs. Sci Data 2023; 10:521. [PMID: 37543626 PMCID: PMC10404222 DOI: 10.1038/s41597-023-02432-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/31/2023] [Indexed: 08/07/2023] Open
Abstract
Digital radiography is one of the most common and cost-effective standards for the diagnosis of bone fractures. For such diagnoses expert intervention is required which is time-consuming and demands rigorous training. With the recent growth of computer vision algorithms, there is a surge of interest in computer-aided diagnosis. The development of algorithms demands large datasets with proper annotations. Existing X-Ray datasets are either small or lack proper annotation, which hinders the development of machine-learning algorithms and evaluation of the relative performance of algorithms for classification, localization, and segmentation. We present FracAtlas, a new dataset of X-Ray scans curated from the images collected from 3 major hospitals in Bangladesh. Our dataset includes 4,083 images that have been manually annotated for bone fracture classification, localization, and segmentation with the help of 2 expert radiologists and an orthopedist using the open-source labeling platform, makesense.ai. There are 717 images with 922 instances of fractures. Each of the fracture instances has its own mask and bounding box, whereas the scans also have global labels for classification tasks. We believe the dataset will be a valuable resource for researchers interested in developing and evaluating machine learning algorithms for bone fracture diagnosis.
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Affiliation(s)
- Iftekharul Abedeen
- Islamic University of Technology, Gazipur, 1704, Bangladesh
- United International University, Dhaka, 1212, Bangladesh
| | - Md Ashiqur Rahman
- Islamic University of Technology, Gazipur, 1704, Bangladesh
- United International University, Dhaka, 1212, Bangladesh
| | | | - Tasnim Ahmed
- Islamic University of Technology, Gazipur, 1704, Bangladesh.
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Gasmi I, Calinghen A, Parienti JJ, Belloy F, Fohlen A, Pelage JP. Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children. Pediatr Radiol 2023; 53:1675-1684. [PMID: 36877239 DOI: 10.1007/s00247-023-05621-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 11/21/2022] [Accepted: 01/30/2023] [Indexed: 03/07/2023]
Abstract
BACKGROUND Advances have been made in the use of artificial intelligence (AI) in the field of diagnostic imaging, particularly in the detection of fractures on conventional radiographs. Studies looking at the detection of fractures in the pediatric population are few. The anatomical variations and evolution according to the child's age require specific studies of this population. Failure to diagnose fractures early in children may lead to serious consequences for growth. OBJECTIVE To evaluate the performance of an AI algorithm based on deep neural networks toward detecting traumatic appendicular fractures in a pediatric population. To compare sensitivity, specificity, positive predictive value and negative predictive value of different readers and the AI algorithm. MATERIALS AND METHODS This retrospective study conducted on 878 patients younger than 18 years of age evaluated conventional radiographs obtained after recent non-life-threatening trauma. All radiographs of the shoulder, arm, elbow, forearm, wrist, hand, leg, knee, ankle and foot were evaluated. The diagnostic performance of a consensus of radiology experts in pediatric imaging (reference standard) was compared with those of pediatric radiologists, emergency physicians, senior residents and junior residents. The predictions made by the AI algorithm and the annotations made by the different physicians were compared. RESULTS The algorithm predicted 174 fractures out of 182, corresponding to a sensitivity of 95.6%, a specificity of 91.64% and a negative predictive value of 98.76%. The AI predictions were close to that of pediatric radiologists (sensitivity 98.35%) and that of senior residents (95.05%) and were above those of emergency physicians (81.87%) and junior residents (90.1%). The algorithm identified 3 (1.6%) fractures not initially seen by pediatric radiologists. CONCLUSION This study suggests that deep learning algorithms can be useful in improving the detection of fractures in children.
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Affiliation(s)
- Idriss Gasmi
- Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France
| | - Arvin Calinghen
- Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France
| | - Jean-Jacques Parienti
- GRAM 2.0 EA2656 UNICAEN Normandie, University Hospital, Caen, France
- Department of Clinical Research, Caen University Hospital, Caen, France
| | - Frederique Belloy
- Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France
| | - Audrey Fohlen
- Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France
- UNICAEN CEA CNRS ISTCT- CERVOxy, Normandie University, 14000, Caen, France
| | - Jean-Pierre Pelage
- Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France.
- UNICAEN CEA CNRS ISTCT- CERVOxy, Normandie University, 14000, Caen, France.
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Kim T, Moon NH, Goh TS, Jung ID. Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence. Sci Rep 2023; 13:10415. [PMID: 37369833 DOI: 10.1038/s41598-023-37560-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/23/2023] [Indexed: 06/29/2023] Open
Abstract
One of the key aspects of the diagnosis and treatment of atypical femoral fractures is the early detection of incomplete fractures and the prevention of their progression to complete fractures. However, an incomplete atypical femoral fracture can be misdiagnosed as a normal lesion by both primary care physicians and orthopedic surgeons; expert consultation is needed for accurate diagnosis. To overcome this limitation, we developed a transfer learning-based ensemble model to detect and localize fractures. A total of 1050 radiographs, including 100 incomplete fractures, were preprocessed by applying a Sobel filter. Six models (EfficientNet B5, B6, B7, DenseNet 121, MobileNet V1, and V2) were selected for transfer learning. We then composed two ensemble models; the first was based on the three models having the highest accuracy, and the second was based on the five models having the highest accuracy. The area under the curve (AUC) of the case that used the three most accurate models was the highest at 0.998. This study demonstrates that an ensemble of transfer-learning-based models can accurately classify and detect fractures, even in an imbalanced dataset. This artificial intelligence (AI)-assisted diagnostic application could support decision-making and reduce the workload of clinicians with its high speed and accuracy.
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Affiliation(s)
- Taekyeong Kim
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Nam Hoon Moon
- Department of Orthopaedic Surgery, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, 49241, Republic of Korea
| | - Tae Sik Goh
- Department of Orthopaedic Surgery, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, 49241, Republic of Korea
| | - Im Doo Jung
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
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Anwar T, Anwar H. LSNet: a novel CNN architecture to identify wrist fracture from a small X-ray dataset. Int j inf tecnol 2023; 15:2469-2477. [DOI: 10.1007/s41870-023-01311-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 05/25/2023] [Indexed: 09/01/2023]
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15
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Lee KC, Choi IC, Kang CH, Ahn KS, Yoon H, Lee JJ, Kim BH, Shim E. Clinical Validation of an Artificial Intelligence Model for Detecting Distal Radius, Ulnar Styloid, and Scaphoid Fractures on Conventional Wrist Radiographs. Diagnostics (Basel) 2023; 13:diagnostics13091657. [PMID: 37175048 PMCID: PMC10178713 DOI: 10.3390/diagnostics13091657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/02/2023] [Accepted: 05/06/2023] [Indexed: 05/15/2023] Open
Abstract
This study aimed to assess the feasibility and performance of an artificial intelligence (AI) model for detecting three common wrist fractures: distal radius, ulnar styloid process, and scaphoid. The AI model was trained with a dataset of 4432 images containing both fractured and non-fractured wrist images. In total, 593 subjects were included in the clinical test. Two human experts independently diagnosed and labeled the fracture sites using bounding boxes to build the ground truth. Two novice radiologists also performed the same task, both with and without model assistance. The sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated for each wrist location. The AUC for detecting distal radius, ulnar styloid, and scaphoid fractures per wrist were 0.903 (95% C.I. 0.887-0.918), 0.925 (95% C.I. 0.911-0.939), and 0.808 (95% C.I. 0.748-0.967), respectively. When assisted by the AI model, the scaphoid fracture AUC of the two novice radiologists significantly increased from 0.75 (95% C.I. 0.66-0.83) to 0.85 (95% C.I. 0.77-0.93) and from 0.71 (95% C.I. 0.62-0.80) to 0.80 (95% C.I. 0.71-0.88), respectively. Overall, the developed AI model was found to be reliable for detecting wrist fractures, particularly for scaphoid fractures, which are commonly missed.
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Affiliation(s)
- Kyu-Chong Lee
- Department of Radiology, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - In Cheul Choi
- Department of Orthopedics Surgery, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - Chang Ho Kang
- Department of Radiology, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - Kyung-Sik Ahn
- Department of Radiology, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - Heewon Yoon
- Department of Radiology, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | | | - Baek Hyun Kim
- Department of Radiology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea
| | - Euddeum Shim
- Department of Radiology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea
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Zech JR, Carotenuto G, Igbinoba Z, Tran CV, Insley E, Baccarella A, Wong TT. Detecting pediatric wrist fractures using deep-learning-based object detection. Pediatr Radiol 2023. [PMID: 36650360 DOI: 10.1007/s00247-023-05588-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/09/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND Missed fractures are the leading cause of diagnostic error in the emergency department, and fractures of pediatric bones, particularly subtle wrist fractures, can be misidentified because of their varying characteristics and responses to injury. OBJECTIVE This study evaluated the utility of an object detection deep learning framework for classifying pediatric wrist fractures as positive or negative for fracture, including subtle buckle fractures of the distal radius, and evaluated the performance of this algorithm as augmentation to trainee radiograph interpretation. MATERIALS AND METHODS We obtained 395 posteroanterior wrist radiographs from unique pediatric patients (65% positive for fracture, 30% positive for distal radial buckle fracture) and divided them into train (n = 229), tune (n = 41) and test (n = 125) sets. We trained a Faster R-CNN (region-based convolutional neural network) deep learning object-detection model. Two pediatric and two radiology residents evaluated radiographs initially without the artificial intelligence (AI) assistance, and then subsequently with access to the bounding box generated by the Faster R-CNN model. RESULTS The Faster R-CNN model demonstrated an area under the curve (AUC) of 0.92 (95% confidence interval [CI] 0.87-0.97), accuracy of 88% (n = 110/125; 95% CI 81-93%), sensitivity of 88% (n = 70/80; 95% CI 78-94%) and specificity of 89% (n = 40/45, 95% CI 76-96%) in identifying any fracture and identified 90% of buckle fractures (n = 35/39, 95% CI 76-97%). Access to Faster R-CNN model predictions significantly improved average resident accuracy from 80 to 93% in detecting any fracture (P < 0.001) and from 69 to 92% in detecting buckle fracture (P < 0.001). After accessing AI predictions, residents significantly outperformed AI in cases of disagreement (73% resident correct vs. 27% AI, P = 0.002). CONCLUSION An object-detection-based deep learning approach trained with only a few hundred examples identified radiographs containing pediatric wrist fractures with high accuracy. Access to model predictions significantly improved resident accuracy in diagnosing these fractures.
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Kuo RYL, Harrison C, Curran TA, Jones B, Freethy A, Cussons D, Stewart M, Collins GS, Furniss D. Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis. Radiology 2022; 304:50-62. [PMID: 35348381 DOI: 10.1148/radiol.211785] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies apply artificial intelligence (AI) techniques to fracture detection as an adjunct to clinician diagnosis. Purpose To perform a systematic review and meta-analysis comparing the diagnostic performance in fracture detection between AI and clinicians in peer-reviewed publications and the gray literature (ie, articles published on preprint repositories). Materials and Methods A search of multiple electronic databases between January 2018 and July 2020 (updated June 2021) was performed that included any primary research studies that developed and/or validated AI for the purposes of fracture detection at any imaging modality and excluded studies that evaluated image segmentation algorithms. Meta-analysis with a hierarchical model to calculate pooled sensitivity and specificity was used. Risk of bias was assessed by using a modified Prediction Model Study Risk of Bias Assessment Tool, or PROBAST, checklist. Results Included for analysis were 42 studies, with 115 contingency tables extracted from 32 studies (55 061 images). Thirty-seven studies identified fractures on radiographs and five studies identified fractures on CT images. For internal validation test sets, the pooled sensitivity was 92% (95% CI: 88, 93) for AI and 91% (95% CI: 85, 95) for clinicians, and the pooled specificity was 91% (95% CI: 88, 93) for AI and 92% (95% CI: 89, 92) for clinicians. For external validation test sets, the pooled sensitivity was 91% (95% CI: 84, 95) for AI and 94% (95% CI: 90, 96) for clinicians, and the pooled specificity was 91% (95% CI: 81, 95) for AI and 94% (95% CI: 91, 95) for clinicians. There were no statistically significant differences between clinician and AI performance. There were 22 of 42 (52%) studies that were judged to have high risk of bias. Meta-regression identified multiple sources of heterogeneity in the data, including risk of bias and fracture type. Conclusion Artificial intelligence (AI) and clinicians had comparable reported diagnostic performance in fracture detection, suggesting that AI technology holds promise as a diagnostic adjunct in future clinical practice. Clinical trial registration no. CRD42020186641 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Cohen and McInnes in this issue.
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Affiliation(s)
- Rachel Y L Kuo
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Conrad Harrison
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Terry-Ann Curran
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Benjamin Jones
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Alexander Freethy
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - David Cussons
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Max Stewart
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Gary S Collins
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Dominic Furniss
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
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Karam C, Zini JE, Awad M, Saade C, Naffaa L, Amine ME. A Progressive and Cross-Domain Deep Transfer Learning Framework for Wrist Fracture Detection. Journal of Artificial Intelligence and Soft Computing Research 2021; 12:101-20. [DOI: 10.2478/jaiscr-2022-0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Abstract
There has been an amplified focus on and benefit from the adoption of artificial intelligence (AI) in medical imaging applications. However, deep learning approaches involve training with massive amounts of annotated data in order to guarantee generalization and achieve high accuracies. Gathering and annotating large sets of training images require expertise which is both expensive and time-consuming, especially in the medical field. Furthermore, in health care systems where mistakes can have catastrophic consequences, there is a general mistrust in the black-box aspect of AI models. In this work, we focus on improving the performance of medical imaging applications when limited data is available while focusing on the interpretability aspect of the proposed AI model. This is achieved by employing a novel transfer learning framework, progressive transfer learning, an automated annotation technique and a correlation analysis experiment on the learned representations.
Progressive transfer learning helps jump-start the training of deep neural networks while improving the performance by gradually transferring knowledge from two source tasks into the target task. It is empirically tested on the wrist fracture detection application by first training a general radiology network RadiNet and using its weights to initialize RadiNetwrist
, that is trained on wrist images to detect fractures. Experiments show that RadiNetwrist
achieves an accuracy of 87% and an AUC ROC of 94% as opposed to 83% and 92% when it is pre-trained on the ImageNet dataset.
This improvement in performance is investigated within an explainable AI framework. More concretely, the learned deep representations of RadiNetwrist
are compared to those learned by the baseline model by conducting a correlation analysis experiment. The results show that, when transfer learning is gradually applied, some features are learned earlier in the network. Moreover, the deep layers in the progressive transfer learning framework are shown to encode features that are not encountered when traditional transfer learning techniques are applied.
In addition to the empirical results, a clinical study is conducted and the performance of RadiNetwrist
is compared to that of an expert radiologist. We found that RadiNetwrist
exhibited similar performance to that of radiologists with more than 20 years of experience.
This motivates follow-up research to train on more data to feasibly surpass radiologists’ performance, and investigate the interpretability of AI models in the healthcare domain where the decision-making process needs to be credible and transparent.
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Guermazi A, Tannoury C, Kompel AJ, Murakami AM, Ducarouge A, Gillibert A, Li X, Tournier A, Lahoud Y, Jarraya M, Lacave E, Rahimi H, Pourchot A, Parisien RL, Merritt AC, Comeau D, Regnard NE, Hayashi D. Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence. Radiology 2021; 302:627-636. [PMID: 34931859 DOI: 10.1148/radiol.210937] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Missed fractures are a common cause of diagnostic discrepancy between initial radiographic interpretation and the final read by board-certified radiologists. Purpose To assess the effect of assistance by artificial intelligence (AI) on diagnostic performances of physicians for fractures on radiographs. Materials and Methods This retrospective diagnostic study used the multi-reader, multi-case methodology based on an external multicenter data set of 480 examinations with at least 60 examinations per body region (foot and ankle, knee and leg, hip and pelvis, hand and wrist, elbow and arm, shoulder and clavicle, rib cage, and thoracolumbar spine) between July 2020 and January 2021. Fracture prevalence was set at 50%. The ground truth was determined by two musculoskeletal radiologists, with discrepancies solved by a third. Twenty-four readers (radiologists, orthopedists, emergency physicians, physician assistants, rheumatologists, family physicians) were presented the whole validation data set (n = 480), with and without AI assistance, with a 1-month minimum washout period. The primary analysis had to demonstrate superiority of sensitivity per patient and the noninferiority of specificity per patient at -3% margin with AI aid. Stand-alone AI performance was also assessed using receiver operating characteristic curves. Results A total of 480 patients were included (mean age, 59 years ± 16 [standard deviation]; 327 women). The sensitivity per patient was 10.4% higher (95% CI: 6.9, 13.9; P < .001 for superiority) with AI aid (4331 of 5760 readings, 75.2%) than without AI (3732 of 5760 readings, 64.8%). The specificity per patient with AI aid (5504 of 5760 readings, 95.6%) was noninferior to that without AI aid (5217 of 5760 readings, 90.6%), with a difference of +5.0% (95% CI: +2.0, +8.0; P = .001 for noninferiority). AI shortened the average reading time by 6.3 seconds per examination (95% CI: -12.5, -0.1; P = .046). The sensitivity by patient gain was significant in all regions (+8.0% to +16.2%; P < .05) but shoulder and clavicle and spine (+4.2% and +2.6%; P = .12 and .52). Conclusion AI assistance improved the sensitivity and may even improve the specificity of fracture detection by radiologists and nonradiologists, without lengthening reading time. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Link and Pedoia in this issue.
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Affiliation(s)
- Ali Guermazi
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Chadi Tannoury
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Andrew J Kompel
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Akira M Murakami
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Alexis Ducarouge
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - André Gillibert
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Xinning Li
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Antoine Tournier
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Youmna Lahoud
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Mohamed Jarraya
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Elise Lacave
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Hamza Rahimi
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Aloïs Pourchot
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Robert L Parisien
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Alexander C Merritt
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Douglas Comeau
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Nor-Eddine Regnard
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
| | - Daichi Hayashi
- From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.)
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Oliveira E Carmo L, van den Merkhof A, Olczak J, Gordon M, Jutte PC, Jaarsma RL, IJpma FFA, Doornberg JN, Prijs J. An increasing number of convolutional neural networks for fracture recognition and classification in orthopaedics : are these externally validated and ready for clinical application? Bone Jt Open 2021; 2:879-885. [PMID: 34669518 PMCID: PMC8558452 DOI: 10.1302/2633-1462.210.bjo-2021-0133] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Aims The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs? Methods The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS). Results Out of 1,349 studies, 36 reported development of a CNN for fracture detection and/or classification. Of these, only four (11%) reported a form of EV. One study used temporal EV, one conducted both temporal and geographical EV, and two used geographical EV. When comparing the CNN’s performance on the IV set versus the EV set, the following were found: AUCs of 0.967 (IV) versus 0.975 (EV), 0.976 (IV) versus 0.985 to 0.992 (EV), 0.93 to 0.96 (IV) versus 0.80 to 0.89 (EV), and F1-scores of 0.856 to 0.863 (IV) versus 0.757 to 0.840 (EV). Conclusion The number of externally validated CNNs in orthopaedic trauma for fracture recognition is still scarce. This greatly limits the potential for transfer of these CNNs from the developing institute to another hospital to achieve similar diagnostic performance. We recommend the use of geographical EV and statements such as the Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI), the Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence (SPIRIT-AI) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis–Machine Learning (TRIPOD-ML) to critically appraise performance of CNNs and improve methodological rigor, quality of future models, and facilitate eventual implementation in clinical practice. Cite this article: Bone Jt Open 2021;2(10):879–885.
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Affiliation(s)
- Luisa Oliveira E Carmo
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands
| | - Anke van den Merkhof
- Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia
| | - Jakub Olczak
- Institute of Clinical Sciences, Danderyd University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Max Gordon
- Institute of Clinical Sciences, Danderyd University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Paul C Jutte
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands
| | - Ruurd L Jaarsma
- Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia
| | - Frank F A IJpma
- Department of Trauma Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Groningen, Netherlands
| | - Job N Doornberg
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands.,Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia.,Department of Trauma Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Groningen, Netherlands
| | - Jasper Prijs
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands.,Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia.,Department of Trauma Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Groningen, Netherlands
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- Machine Learning Consortium
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Reichert G, Bellamine A, Fontaine M, Naipeanu B, Altar A, Mejean E, Javaud N, Siauve N. How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room? J Imaging 2021; 7:105. [DOI: 10.3390/jimaging7070105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular skeleton fractures and evaluated its performance for detecting traumatic fractures on conventional X-rays in the ER, without the need for training on local data. This algorithm was tested on all patients (N = 125) consulting at the Louis Mourier ER in May 2019 for limb trauma. Patients were selected by two emergency physicians from the clinical database used in the ER. Their X-rays were exported and analyzed by a radiologist. The prediction made by the algorithm and the annotation made by the radiologist were compared. For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%). The algorithm incorrectly predicted a fracture in 14 of the 100 patients without fractures (specificity of 86%). The negative predictive value was 98.85%. This study shows that DL algorithms are potentially valuable diagnostic tools for detecting fractures in the ER and could be used in the training of junior radiologists.
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