1
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Suen K, Zhang R, Kutaiba N. Accuracy of wrist fracture detection on radiographs by artificial intelligence compared to human clinicians. A systematic review and meta-analysis. Eur J Radiol 2024; 178:111593. [PMID: 38981178 DOI: 10.1016/j.ejrad.2024.111593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 06/23/2024] [Accepted: 06/28/2024] [Indexed: 07/11/2024]
Abstract
PURPOSE The aim of the study is to perform a systematic review and meta-analysis comparing the diagnostic performance of artificial intelligence (AI) and human readers in the detection of wrist fractures. METHOD This study conducted a systematic review following PRISMA guidelines. Medline and Embase databases were searched for relevant articles published up to August 14, 2023. All included studies reported the diagnostic performance of AI to detect wrist fractures, with or without comparison to human readers. A meta-analysis was performed to calculate the pooled sensitivity and specificity of AI and human experts in detecting distal radius, and scaphoid fractures respectively. RESULTS Of 213 identified records, 20 studies were included after abstract screening and full-text review. Nine articles examined distal radius fractures, while eight studies examined scaphoid fractures. One study included distal radius and scaphoid fractures, and two studies examined paediatric distal radius fractures. The pooled sensitivity and specificity for AI in detecting distal radius fractures were 0.92 (95% CI 0.88-0.95) and 0.89 (0.84-0.92), respectively. The corresponding values for human readers were 0.95 (0.91-0.97) and 0.94 (0.91-0.96). For scaphoid fractures, pooled sensitivity and specificity for AI were 0.85 (0.73-0.92) and 0.83 (0.76-0.89), while human experts exhibited 0.71 (0.66-0.76) and 0.93 (0.90-0.95), respectively. CONCLUSION The results indicate comparable diagnostic accuracy between AI and human readers, especially for distal radius fractures. For the detection of scaphoid fractures, the human readers were similarly sensitive but more specific. These findings underscore the potential of AI to enhance fracture detection accuracy and improve clinical workflow, rather than to replace human intelligence.
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Affiliation(s)
- Kary Suen
- Department of Radiology, Austin Health, Victoria, Australia.
| | - Richard Zhang
- Department of Radiology, Austin Health, Victoria, Australia
| | - Numan Kutaiba
- Department of Radiology, Austin Health, Victoria, Australia
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2
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Kraus M, Anteby R, Konen E, Eshed I, Klang E. Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis. Eur Radiol 2024; 34:4341-4351. [PMID: 38097728 PMCID: PMC11213739 DOI: 10.1007/s00330-023-10473-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 06/29/2024]
Abstract
OBJECTIVES Scaphoid fractures are usually diagnosed using X-rays, a low-sensitivity modality. Artificial intelligence (AI) using Convolutional Neural Networks (CNNs) has been explored for diagnosing scaphoid fractures in X-rays. The aim of this systematic review and meta-analysis is to evaluate the use of AI for detecting scaphoid fractures on X-rays and analyze its accuracy and usefulness. MATERIALS AND METHODS This study followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy. A literature search was conducted in the PubMed database for original articles published until July 2023. The risk of bias and applicability were evaluated using the QUADAS-2 tool. A bivariate diagnostic random-effects meta-analysis was conducted, and the results were analyzed using the Summary Receiver Operating Characteristic (SROC) curve. RESULTS Ten studies met the inclusion criteria and were all retrospective. The AI's diagnostic performance for detecting scaphoid fractures ranged from AUC 0.77 to 0.96. Seven studies were included in the meta-analysis, with a total of 3373 images. The meta-analysis pooled sensitivity and specificity were 0.80 and 0.89, respectively. The meta-analysis overall AUC was 0.88. The QUADAS-2 tool found high risk of bias and concerns about applicability in 9 out of 10 studies. CONCLUSIONS The current results of AI's diagnostic performance for detecting scaphoid fractures in X-rays show promise. The results show high overall sensitivity and specificity and a high SROC result. Further research is needed to compare AI's diagnostic performance to human diagnostic performance in a clinical setting. CLINICAL RELEVANCE STATEMENT Scaphoid fractures are prone to be missed secondary to assessment with a low sensitivity modality and a high occult fracture rate. AI systems can be beneficial for clinicians and radiologists to facilitate early diagnosis, and avoid missed injuries. KEY POINTS • Scaphoid fractures are common and some can be easily missed in X-rays. • Artificial intelligence (AI) systems demonstrate high diagnostic performance for the diagnosis of scaphoid fractures in X-rays. • AI systems can be beneficial in diagnosing both obvious and occult scaphoid fractures.
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Affiliation(s)
- Matan Kraus
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel.
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Roi Anteby
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of General Surgery, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
| | - Eli Konen
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Iris Eshed
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eyal Klang
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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3
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Su JH, Tung YC, Liao YW, Wang HY, Chen BH, Chang CD, Cheng YF, Chang WC, Chin CY. Deep Learning-Based Surgical Treatment Recommendation and Nonsurgical Prognosis Status Classification for Scaphoid Fractures by Automated X-ray Image Recognition. Biomedicines 2024; 12:1198. [PMID: 38927405 PMCID: PMC11201164 DOI: 10.3390/biomedicines12061198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024] Open
Abstract
Biomedical information retrieval for diagnosis, treatment and prognosis has been studied for a long time. In particular, image recognition using deep learning has been shown to be very effective for cancers and diseases. In these fields, scaphoid fracture recognition is a hot topic because the appearance of scaphoid fractures is not easy to detect. Although there have been a number of recent studies on this topic, no studies focused their attention on surgical treatment recommendations and nonsurgical prognosis status classification. Indeed, a successful treatment recommendation will assist the doctor in selecting an effective treatment, and the prognosis status classification will help a radiologist recognize the image more efficiently. For these purposes, in this paper, we propose potential solutions through a comprehensive empirical study assessing the effectiveness of recent deep learning techniques on surgical treatment recommendation and nonsurgical prognosis status classification. In the proposed system, the scaphoid is firstly segmented from an unknown X-ray image. Next, for surgical treatment recommendation, the fractures are further filtered and recognized. According to the recognition result, the surgical treatment recommendation is generated. Finally, even without sufficient fracture information, the doctor can still make an effective decision to opt for surgery or not. Moreover, for nonsurgical patients, the current prognosis status of avascular necrosis, non-union and union can be classified. The related experimental results made using a real dataset reveal that the surgical treatment recommendation reached 80% and 86% in accuracy and AUC (Area Under the Curve), respectively, while the nonsurgical prognosis status classification reached 91% and 96%, respectively. Further, the methods using transfer learning and data augmentation can bring out obvious improvements, which, on average, reached 21.9%, 28.9% and 5.6%, 7.8% for surgical treatment recommendations and nonsurgical prognosis image classification, respectively. Based on the experimental results, the recommended methods in this paper are DenseNet169 and ResNet50 for surgical treatment recommendation and nonsurgical prognosis status classification, respectively. We believe that this paper can provide an important reference for future research on surgical treatment recommendation and nonsurgical prognosis classification for scaphoid fractures.
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Affiliation(s)
- Ja-Hwung Su
- Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan;
| | - Yu-Cheng Tung
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (C.-D.C.); (Y.-F.C.); (W.-C.C.)
| | - Yi-Wen Liao
- Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 82444, Taiwan; (Y.-W.L.); (H.-Y.W.)
| | - Hung-Yu Wang
- Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 82444, Taiwan; (Y.-W.L.); (H.-Y.W.)
| | - Bo-Hong Chen
- Department of Information Management, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan;
| | - Ching-Di Chang
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (C.-D.C.); (Y.-F.C.); (W.-C.C.)
| | - Yu-Fan Cheng
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (C.-D.C.); (Y.-F.C.); (W.-C.C.)
| | - Wan-Ching Chang
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (C.-D.C.); (Y.-F.C.); (W.-C.C.)
| | - Chu-Yu Chin
- Telecommunication Laboratories Chunghwa Telecom Company Limited, Kaohsiung 80002, Taiwan;
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4
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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] [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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5
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Bhatnagar A, Kekatpure AL, Velagala VR, Kekatpure A. A Review on the Use of Artificial Intelligence in Fracture Detection. Cureus 2024; 16:e58364. [PMID: 38756254 PMCID: PMC11097122 DOI: 10.7759/cureus.58364] [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: 10/28/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) simulates intelligent behavior using computers with minimum human intervention. Recent advances in AI, especially deep learning, have made significant progress in perceptual operations, enabling computers to convey and comprehend complicated input more accurately. Worldwide, fractures affect people of all ages and in all regions of the planet. One of the most prevalent causes of inaccurate diagnosis and medical lawsuits is overlooked fractures on radiographs taken in the emergency room, which can range from 2% to 9%. The workforce will soon be under a great deal of strain due to the growing demand for fracture detection on multiple imaging modalities. A dearth of radiologists worsens this rise in demand as a result of a delay in hiring and a significant percentage of radiologists close to retirement. Additionally, the process of interpreting diagnostic images can sometimes be challenging and tedious. Integrating orthopedic radio-diagnosis with AI presents a promising solution to these problems. There has recently been a noticeable rise in the application of deep learning techniques, namely convolutional neural networks (CNNs), in medical imaging. In the field of orthopedic trauma, CNNs are being documented to operate at the proficiency of expert orthopedic surgeons and radiologists in the identification and categorization of fractures. CNNs can analyze vast amounts of data at a rate that surpasses that of human observations. In this review, we discuss the use of deep learning methods in fracture detection and classification, the integration of AI with various imaging modalities, and the benefits and disadvantages of integrating AI with radio-diagnostics.
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Affiliation(s)
- Aayushi Bhatnagar
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aditya L Kekatpure
- Orthopedic Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Vivek R Velagala
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aashay Kekatpure
- Orthopedic Surgery, Narendra Kumar Prasadrao Salve Institute of Medical Sciences and Research, Nagpur, IND
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6
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Wang H, Ying J, Liu J, Yu T, Huang D. Harnessing ResNet50 and SENet for enhanced ankle fracture identification. BMC Musculoskelet Disord 2024; 25:250. [PMID: 38561697 PMCID: PMC10983628 DOI: 10.1186/s12891-024-07355-8] [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: 10/29/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Ankle fractures are prevalent injuries that necessitate precise diagnostic tools. Traditional diagnostic methods have limitations that can be addressed using machine learning techniques, with the potential to improve accuracy and expedite diagnoses. METHODS We trained various deep learning architectures, notably the Adapted ResNet50 with SENet capabilities, to identify ankle fractures using a curated dataset of radiographic images. Model performance was evaluated using common metrics like accuracy, precision, and recall. Additionally, Grad-CAM visualizations were employed to interpret model decisions. RESULTS The Adapted ResNet50 with SENet capabilities consistently outperformed other models, achieving an accuracy of 93%, AUC of 95%, and recall of 92%. Grad-CAM visualizations provided insights into areas of the radiographs that the model deemed significant in its decisions. CONCLUSIONS The Adapted ResNet50 model enhanced with SENet capabilities demonstrated superior performance in detecting ankle fractures, offering a promising tool to complement traditional diagnostic methods. However, continuous refinement and expert validation are essential to ensure optimal application in clinical settings.
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Grants
- 2020AS0031 Science and Technology Projects in the Field of Agriculture and Social Development in Yinzhou District, Ningbo City, Zhejiang Province, China
- 2020AS0031 Science and Technology Projects in the Field of Agriculture and Social Development in Yinzhou District, Ningbo City, Zhejiang Province, China
- 2020AS0031 Science and Technology Projects in the Field of Agriculture and Social Development in Yinzhou District, Ningbo City, Zhejiang Province, China
- 2020AS0031 Science and Technology Projects in the Field of Agriculture and Social Development in Yinzhou District, Ningbo City, Zhejiang Province, China
- 2020AS0031 Science and Technology Projects in the Field of Agriculture and Social Development in Yinzhou District, Ningbo City, Zhejiang Province, China
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Affiliation(s)
- Hua Wang
- Department of Medical Imaging, Ningbo No. 6 Hospital, Ningbo, China
| | - Jichong Ying
- Department of Orthopedics, Ningbo No. 6 Hospital, Ningbo, China
| | - Jianlei Liu
- Department of Orthopedics, Ningbo No. 6 Hospital, Ningbo, China
| | - Tianming Yu
- Department of Orthopedics, Ningbo No. 6 Hospital, Ningbo, China
| | - Dichao Huang
- Department of Orthopedics, Ningbo No. 6 Hospital, Ningbo, China.
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7
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Su Z, Adam A, Nasrudin MF, Ayob M, Punganan G. Skeletal Fracture Detection with Deep Learning: A Comprehensive Review. Diagnostics (Basel) 2023; 13:3245. [PMID: 37892066 PMCID: PMC10606060 DOI: 10.3390/diagnostics13203245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. However, challenges remain that hinder progress in this field. Firstly, a lack of clear definitions for recognition, classification, detection, and localization tasks hampers the consistent development and comparison of methodologies. The existing reviews often lack technical depth or have limited scope. Additionally, the absence of explainable facilities undermines the clinical application and expert confidence in results. To address these issues, this comprehensive review analyzes and evaluates 40 out of 337 recent papers identified in prestigious databases, including WOS, Scopus, and EI. The objectives of this review are threefold. Firstly, precise definitions are established for the bone fracture recognition, classification, detection, and localization tasks within deep learning. Secondly, each study is summarized based on key aspects such as the bones involved, research objectives, dataset sizes, methods employed, results obtained, and concluding remarks. This process distills the diverse approaches into a generalized processing framework or workflow. Moreover, this review identifies the crucial areas for future research in deep learning models for bone fracture diagnosis. These include enhancing the network interpretability, integrating multimodal clinical information, providing therapeutic schedule recommendations, and developing advanced visualization methods for clinical application. By addressing these challenges, deep learning models can be made more intelligent and specialized in this domain. In conclusion, this review fills the gap in precise task definitions within deep learning for bone fracture diagnosis and provides a comprehensive analysis of the recent research. The findings serve as a foundation for future advancements, enabling improved interpretability, multimodal integration, clinical decision support, and advanced visualization techniques.
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Affiliation(s)
- Zhihao Su
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Afzan Adam
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Mohammad Faidzul Nasrudin
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Masri Ayob
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Gauthamen Punganan
- Department of Orthopedics and Traumatology, Hospital Raja Permaisuri Bainun, Ipoh 30450, Perak, Malaysia
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Orji C, Reghefaoui M, Saavedra Palacios MS, Thota P, Peresuodei TS, Gill A, Hamid P. Application of Artificial Intelligence and Machine Learning in Diagnosing Scaphoid Fractures: A Systematic Review. Cureus 2023; 15:e47732. [PMID: 38021992 PMCID: PMC10676208 DOI: 10.7759/cureus.47732] [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: 09/14/2023] [Accepted: 10/26/2023] [Indexed: 12/01/2023] Open
Abstract
The integration of artificial intelligence (AI) in healthcare has sparked interest in its potential to revolutionize medical diagnostics. This systematic review explores the application of AI and machine learning (ML) techniques in diagnosing scaphoid fractures, which account for a significant percentage of carpal bone fractures and have important implications for wrist function. Scaphoid fractures, common in young and active individuals, require an early and accurate diagnosis for effective treatment. AI has the potential to automate and improve the accuracy of scaphoid fracture detection on radiography, aiding in early diagnosis and reducing unnecessary clinical examinations. This systematic review discusses the methods used to identify relevant studies, including search criteria and quality assessment tools, and presents the results of the selected studies. The findings indicate that AI-driven methods can improve diagnostic accuracy, reducing the risk of missed fractures and complications. AI assistance can also alleviate the workload of medical professionals, improving diagnostic efficiency and reducing observer fatigue. However, challenges such as algorithm limitations and the need for continuous refinement must be addressed to ensure reliable fracture identification. This review underscores the clinical significance of AI-assisted diagnostics, especially in cases where fractures may be subtle or occult. It emphasizes the importance of integrating AI into medical education and training and calls for robust data collection and collaboration between AI developers and medical practitioners. Future research should focus on larger datasets, algorithm improvement, cost-effectiveness assessment, and international partnerships to fully harness the potential of AI in diagnosing scaphoid fractures.
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Affiliation(s)
- Chijioke Orji
- Trauma and Orthopaedics, California Institute of Behavioral Neurosciences & Psychology, California, USA
| | | | | | - Priyanka Thota
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, California, USA
| | | | - Abhishek Gill
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, California, USA
| | - Pousette Hamid
- Neurology, California Institute of Behavioral Neurosciences & Psychology, California, USA
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9
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Hendrix N, Hendrix W, van Dijke K, Maresch B, Maas M, Bollen S, Scholtens A, de Jonge M, Ong LLS, van Ginneken B, Rutten M. Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist. Eur Radiol 2023; 33:1575-1588. [PMID: 36380195 PMCID: PMC9935716 DOI: 10.1007/s00330-022-09205-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/19/2022] [Accepted: 09/25/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVES To assess how an artificial intelligence (AI) algorithm performs against five experienced musculoskeletal radiologists in diagnosing scaphoid fractures and whether it aids their diagnosis on conventional multi-view radiographs. METHODS Four datasets of conventional hand, wrist, and scaphoid radiographs were retrospectively acquired at two hospitals (hospitals A and B). Dataset 1 (12,990 radiographs from 3353 patients, hospital A) and dataset 2 (1117 radiographs from 394 patients, hospital B) were used for training and testing a scaphoid localization and laterality classification component. Dataset 3 (4316 radiographs from 840 patients, hospital A) and dataset 4 (688 radiographs from 209 patients, hospital B) were used for training and testing the fracture detector. The algorithm was compared with the radiologists in an observer study. Evaluation metrics included sensitivity, specificity, positive predictive value (PPV), area under the characteristic operating curve (AUC), Cohen's kappa coefficient (κ), fracture localization precision, and reading time. RESULTS The algorithm detected scaphoid fractures with a sensitivity of 72%, specificity of 93%, PPV of 81%, and AUC of 0.88. The AUC of the algorithm did not differ from each radiologist (0.87 [radiologists' mean], p ≥ .05). AI assistance improved five out of ten pairs of inter-observer Cohen's κ agreements (p < .05) and reduced reading time in four radiologists (p < .001), but did not improve other metrics in the majority of radiologists (p ≥ .05). CONCLUSIONS The AI algorithm detects scaphoid fractures on conventional multi-view radiographs at the level of five experienced musculoskeletal radiologists and could significantly shorten their reading time. KEY POINTS • An artificial intelligence algorithm automatically detects scaphoid fractures on conventional multi-view radiographs at the same level of five experienced musculoskeletal radiologists. • There is preliminary evidence that automated scaphoid fracture detection can significantly shorten the reading time of musculoskeletal radiologists.
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Affiliation(s)
- Nils Hendrix
- Radiology Department, Jeroen Bosch Ziekenhuis, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, the Netherlands.
- Jheronimus Academy of Data Science, Sint Janssingel 92, 5211 DA, 's-Hertogenbosch, the Netherlands.
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands.
| | - Ward Hendrix
- Radiology Department, Jeroen Bosch Ziekenhuis, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands
| | - Kees van Dijke
- Radiology Department, Noordwest Ziekenhuisgroep, Wilhelminalaan 12, 1815JD, Alkmaar, the Netherlands
| | - Bas Maresch
- Radiology Department, Ziekenhuis Gelderse Vallei, Willy Brandtlaan 10, 6717 RP, Ede, the Netherlands
| | - Mario Maas
- Radiology and Nuclear Medicine Department, Academic Medical Center, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands
| | - Stijn Bollen
- Radiology Department, Groene Hart Ziekenhuis, Bleulandweg 10, 2803 HH, Gouda, the Netherlands
| | - Alexander Scholtens
- Radiology and Nuclear Medicine Department, Tergooi, Van Riebeeckweg 212, 1213 XZ, Hilversum, the Netherlands
| | - Milko de Jonge
- Radiology Department, St. Antonius Ziekenhuis, Soestwetering 1, 3543 AZ, Utrecht, the Netherlands
| | - Lee-Ling Sharon Ong
- Jheronimus Academy of Data Science, Sint Janssingel 92, 5211 DA, 's-Hertogenbosch, the Netherlands
- Cognitive Science and Artificial Intelligence Department, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, the Netherlands
| | - Bram van Ginneken
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands
| | - Matthieu Rutten
- Radiology Department, Jeroen Bosch Ziekenhuis, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands
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10
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Cohen M, Puntonet J, Sanchez J, Kierszbaum E, Crema M, Soyer P, Dion E. Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs. Eur Radiol 2022; 33:3974-3983. [PMID: 36515712 DOI: 10.1007/s00330-022-09349-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 09/05/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To compare the performances of artificial intelligence (AI) to those of radiologists in wrist fracture detection on radiographs. METHODS This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI) RESULTS: A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78-87%) was significantly greater than that of IRR (76%; 95% CI: 70-81%) (p < 0.001). Specificities were similar for AI (96%; 95% CI: 93-97%) and for IRR (96%; 95% CI: 94-98%) (p = 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84-92%) compared to AI and IRR (p < 0.001) and a lower specificity (92%; 95% CI: 89-95%) (p < 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR). CONCLUSIONS Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist's analysis yields best performances. KEY POINTS • Artificial intelligence has better performances for wrist fracture detection compared to non-expert radiologists in daily practice. • Performance of artificial intelligence greatly differs depending on the anatomical area. • Sensitivity of artificial intelligence for the detection of carpal bones fractures is 56%.
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Affiliation(s)
- Mathieu Cohen
- Department of Radiology - Hotel Dieu Hospital, Assistance Publique-Hopitaux de Paris, Paris, France
- Université Paris Cité, F-75006, Paris, France
| | - Julien Puntonet
- Department of Radiology - Hotel Dieu Hospital, Assistance Publique-Hopitaux de Paris, Paris, France.
- Université Paris Cité, F-75006, Paris, France.
| | - Julien Sanchez
- Université Paris Cité, F-75006, Paris, France
- Institute of Sports Imaging, French National Institute of Sports (INSEP), Paris, France
| | | | - Michel Crema
- Department of Radiology - Hotel Dieu Hospital, Assistance Publique-Hopitaux de Paris, Paris, France
- Institute of Sports Imaging, French National Institute of Sports (INSEP), Paris, France
| | - Philippe Soyer
- Université Paris Cité, F-75006, Paris, France
- Department of Radiology- Cochin Hospital, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Elisabeth Dion
- Department of Radiology - Hotel Dieu Hospital, Assistance Publique-Hopitaux de Paris, Paris, France
- Université Paris Cité, F-75006, Paris, France
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