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Ma D, Wang Y, Zhang X, Su D, Ma M, Qian B, Yang X, Gao J, Wu Y. 3D U-Net Neural Network Architecture-Assisted LDCT to Acquire Vertebral Morphology Parameters: A Vertebral Morphology Comprehensive Analysis in a Chinese Population. Calcif Tissue Int 2024; 115:362-372. [PMID: 39017691 DOI: 10.1007/s00223-024-01255-8] [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: 01/12/2024] [Accepted: 07/01/2024] [Indexed: 07/18/2024]
Abstract
To evaluate the feasibility of acquiring vertebral height from chest low-dose computed tomography (LDCT) images using an artificial intelligence (AI) system based on 3D U-Net vertebral segmentation technology and the correlation and features of vertebral morphology with sex and age of the Chinese population. Patients who underwent chest LDCT between September 2020 and April 2023 were enrolled. The Altman and Pearson's correlation analyses were used to compare the correlation and consistency between the AI software and manual measurement of vertebral height. The anterior height (Ha), middle height (Hm), posterior height (Hp), and vertebral height ratios (VHRs) (Ha/Hp and Hm/Hp) were measured from T1 to L2 using an AI system. The VHR is the ratio of Ha to Hp or the ratio of Hm to Hp of the vertebrae, which can reflect the shape of the anterior wedge and biconcave vertebrae. Changes in these parameters, particularly the VHR, were analysed at different vertebral levels in different age and sex groups. The results of the AI methods were highly consistent and correlated with manual measurements. The Pearson's correlation coefficients were 0.855, 0.919, and 0.846, respectively. The trend of VHRs showed troughs at T7 and T11 and a peak at T9; however, Hm/Hp showed slight fluctuations. Regarding the VHR, significant sex differences were found at L1 and L2 in all age bands. This innovative study focuses on vertebral morphology for opportunistic analysis in the mainland Chinese population and the distribution tendency of vertebral morphology with ageing using a chest LDCT aided by an AI system based on 3D U-Net vertebral segmentation technology. The AI system demonstrates the potential to automatically perform opportunistic vertebral morphology analyses using LDCT scans obtained during lung cancer screening. We advocate the use of age-, sex-, and vertebral level-specific criteria for the morphometric evaluation of vertebral osteoporotic fractures for a more accurate diagnosis of vertebral fractures and spinal pathologies.
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Affiliation(s)
- Duoshan Ma
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Yan Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Xinxin Zhang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Danyang Su
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Mengze Ma
- Medical 3D Printing Center, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Baoxin Qian
- Dongsheng Science and Technology Park, Room A206, B2, Huiying Medical Technology Co, Ltd, HaiDian District, Beijing City, 100192, China
| | - Xiaopeng Yang
- Medical 3D Printing Center, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Yan Wu
- Medical 3D Printing Center, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China.
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Keller M, Rohner M, Honigmann P. The potential benefit of artificial intelligence regarding clinical decision-making in the treatment of wrist trauma patients. J Orthop Surg Res 2024; 19:579. [PMID: 39294720 PMCID: PMC11411868 DOI: 10.1186/s13018-024-05063-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Accepted: 09/07/2024] [Indexed: 09/21/2024] Open
Abstract
PURPOSE The implementation of artificial intelligence (AI) in health care is gaining popularity. Many publications describe powerful AI-enabled algorithms. Yet there's only scarce evidence for measurable value in terms of patient outcomes, clinical decision-making or socio-economic impact. Our aim was to investigate the significance of AI in the emergency treatment of wrist trauma patients. METHOD Two groups of physicians were confronted with twenty realistic cases of wrist trauma patients and had to find the correct diagnosis and provide a treatment recommendation. One group was assisted by an AI-enabled application which detects and localizes distal radius fractures (DRF) with near-to-perfect precision while the other group had no help. Primary outcome measurement was diagnostic accuracy. Secondary outcome measurements were required time, number of added CT scans and senior consultations, correctness of the treatment, subjective and objective stress levels. RESULTS The AI-supported group was able to make a diagnosis without support (no additional CT, no senior consultation) in significantly more of the cases than the control group (75% vs. 52%, p = 0.003). The AI-enhanced group detected DRF with superior sensitivity (1.00 vs. 0.96, p = 0.06) and specificity (0.99 vs. 0.93, p = 0.17), used significantly less additional CT scans to reach the correct diagnosis (14% vs. 28%, p = 0.02) and was subjectively significantly less stressed (p = 0.05). CONCLUSION The results indicate that physicians can diagnose wrist trauma more accurately and faster when aided by an AI-tool that lessens the need for extra diagnostic procedures. The AI-tool also seems to lower physicians' stress levels while examining cases. We anticipate that these benefits will be amplified in larger studies as skepticism towards the new technology diminishes.
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Affiliation(s)
- Marco Keller
- Hand and Peripheral Nerve Surgery, Department of Orthopaedic Surgery and Traumatology, Kantonsspital Baselland (Bruderholz, Liestal, Laufen), Bruderholz, Switzerland.
- Medical Additive Manufacturing Research Group (MAM), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
- Hand and Peripheral Nerve Surgery, Department of Orthopaedic Surgery, Traumatology and Hand Surgery, Spital Limmattal, Schlieren, Switzerland.
| | - Meret Rohner
- Hand and Peripheral Nerve Surgery, Department of Orthopaedic Surgery and Traumatology, Kantonsspital Baselland (Bruderholz, Liestal, Laufen), Bruderholz, Switzerland
- Medical Additive Manufacturing Research Group (MAM), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
- Medical Faculty, University of Basel, Basel, Switzerland
| | - Philipp Honigmann
- Hand and Peripheral Nerve Surgery, Department of Orthopaedic Surgery and Traumatology, Kantonsspital Baselland (Bruderholz, Liestal, Laufen), Bruderholz, Switzerland
- Medical Additive Manufacturing Research Group (MAM), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
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Yari A, Fasih P, Hosseini Hooshiar M, Goodarzi A, Fattahi SF. Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence. Dentomaxillofac Radiol 2024; 53:363-371. [PMID: 38652576 PMCID: PMC11358630 DOI: 10.1093/dmfr/twae018] [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: 02/26/2024] [Revised: 04/11/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024] Open
Abstract
OBJECTIVES This study evaluated the performance of the YOLOv5 deep learning model in detecting different mandibular fracture types in panoramic images. METHODS The dataset of panoramic radiographs with mandibular fractures was divided into training, validation, and testing sets, with 60%, 20%, and 20% of the images, respectively. An equal number of control images without fractures were also distributed among the datasets. The YOLOv5 algorithm was trained to detect six mandibular fracture types based on the anatomical location including symphysis, body, angle, ramus, condylar neck, and condylar head. Performance metrics of accuracy, precision, sensitivity (recall), specificity, dice coefficient (F1 score), and area under the curve (AUC) were calculated for each class. RESULTS A total of 498 panoramic images containing 673 fractures were collected. The accuracy was highest in detecting body (96.21%) and symphysis (95.87%), and was lowest in angle (90.51%) fractures. The highest and lowest precision values were observed in detecting symphysis (95.45%) and condylar head (63.16%) fractures, respectively. The sensitivity was highest in the body (96.67%) fractures and was lowest in the condylar head (80.00%) and condylar neck (81.25%) fractures. The highest specificity was noted in symphysis (98.96%), body (96.08%), and ramus (96.04%) fractures, respectively. The dice coefficient and AUC were highest in detecting body fractures (0.921 and 0.942, respectively), and were lowest in detecting condylar head fractures (0.706 and 0.812, respectively). CONCLUSION The trained algorithm achieved promising results in detecting most fracture types, particularly in body and symphysis regions indicating machine learning potential as a diagnostic aid for clinicians.
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Affiliation(s)
- Amir Yari
- Department of Oral and Maxillofacial Surgery, School of Dentistry, Kashan University of Medical Sciences, Kashan, 8715973474, Iran
| | - Paniz Fasih
- Department of Prosthodontics, School of Dentistry, Kashan University of Medical Sciences, Kashan, 8715973474, Iran
| | - Mohammad Hosseini Hooshiar
- Department of Periodontics, School of Dentistry, Tehran University of Medical Sciences, Tehran, 1439955991, Iran
| | - Ali Goodarzi
- Department of Oral and Maxillofacial Surgery, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, 7195615878, Iran
| | - Seyedeh Farnaz Fattahi
- Department of Prosthodontics, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, 7195615878, Iran
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Yao YC, Lin CL, Chen HH, Lin HH, Hsiung W, Wang ST, Sun YC, Tang YH, Chou PH. Development and validation of deep learning models for identifying the brand of pedicle screws on plain spine radiographs. JOR Spine 2024; 7:e70001. [PMID: 39291095 PMCID: PMC11406509 DOI: 10.1002/jsp2.70001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 07/18/2024] [Accepted: 08/18/2024] [Indexed: 09/19/2024] Open
Abstract
Background In spinal revision surgery, previous pedicle screws (PS) may need to be replaced with new implants. Failure to accurately identify the brand of PS-based instrumentation preoperatively may increase the risk of perioperative complications. This study aimed to develop and validate an optimal deep learning (DL) model to identify the brand of PS-based instrumentation on plain radiographs of spine (PRS) using anteroposterior (AP) and lateral images. Methods A total of 529 patients who received PS-based instrumentation from seven manufacturers were enrolled in this retrospective study. The postoperative PRS were gathered as ground truths. The training, validation, and testing datasets contained 338, 85, and 106 patients, respectively. YOLOv5 was used to crop out the screws' trajectory, and the EfficientNet-b0 model was used to develop single models (AP, Lateral, Merge, and Concatenated) based on the different PRS images. The ensemble models were different combinations of the single models. Primary outcomes were the models' performance in accuracy, sensitivity, precision, F1-score, kappa value, and area under the curve (AUC). Secondary outcomes were the relative performance of models versus human readers and external validation of the DL models. Results The Lateral model had the most stable performance among single models. The discriminative performance was improved by the ensemble method. The AP + Lateral ensemble model had the most stable performance, with an accuracy of 0.9434, F1 score of 0.9388, and AUC of 0.9834. The performance of the ensemble models was comparable to that of experienced orthopedic surgeons and superior to that of inexperienced orthopedic surgeons. External validation revealed that the Lat + Concat ensemble model had the best accuracy (0.9412). Conclusion The DL models demonstrated stable performance in identifying the brand of PS-based instrumentation based on AP and/or lateral images of PRS, which may assist orthopedic spine surgeons in preoperative revision planning in clinical practice.
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Affiliation(s)
- Yu-Cheng Yao
- School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan
- Department of Orthopedics and Traumatology Taipei Veterans General Hospital Taipei Taiwan
| | - Cheng-Li Lin
- Department of Orthopaedic Surgery, National Cheng Kung University Hospital, College of Medicine National Cheung Kung University Tainan Taiwan
| | - Hung-Hsun Chen
- Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City Taiwan
| | - Hsi-Hsien Lin
- School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan
- Department of Orthopedics and Traumatology Taipei Veterans General Hospital Taipei Taiwan
| | - Wei Hsiung
- School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan
- Department of Orthopedics and Traumatology Taipei Veterans General Hospital Taipei Taiwan
- Department of Orthopedics Shin Kong Wu Ho-Su Memorial Hospital Taipei Taiwan
| | - Shih-Tien Wang
- School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan
- Department of Orthopedics and Traumatology Taipei Veterans General Hospital Taipei Taiwan
- Kinmen Hospital Ministry of Health and Welfare Kinmen Taiwan
| | - Ying-Chou Sun
- School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan
- Department of Radiology Taipei Veterans General Hospital Taipei Taiwan
- Department of Medical Imaging and Radiological Technology Yuanpei University of Medical Technology Hsinchu Taiwan
| | - Yu-Hsuan Tang
- Department of Medical Imaging and Radiological Technology Yuanpei University of Medical Technology Hsinchu Taiwan
| | - Po-Hsin Chou
- School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan
- Department of Orthopedics and Traumatology Taipei Veterans General Hospital Taipei Taiwan
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Marullo G, Ulrich L, Antonaci FG, Audisio A, Aprato A, Massè A, Vezzetti E. Classification of AO/OTA 31A/B femur fractures in X-ray images using YOLOv8 and advanced data augmentation techniques. Bone Rep 2024; 22:101801. [PMID: 39324016 PMCID: PMC11422035 DOI: 10.1016/j.bonr.2024.101801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 08/20/2024] [Accepted: 09/05/2024] [Indexed: 09/27/2024] Open
Abstract
Femur fractures are a significant worldwide public health concern that affects patients as well as their families because of their high frequency, morbidity, and mortality. When employing computer-aided diagnostic (CAD) technologies, promising results have been shown in the efficiency and accuracy of fracture classification, particularly with the growing use of Deep Learning (DL) approaches. Nevertheless, the complexity is further increased by the need to collect enough input data to train these algorithms and the challenge of interpreting the findings. By improving on the results of the most recent deep learning-based Arbeitsgemeinschaft für Osteosynthesefragen and Orthopaedic Trauma Association (AO/OTA) system classification of femur fractures, this study intends to support physicians in making correct and timely decisions regarding patient care. A state-of-the-art architecture, YOLOv8, was used and refined while paying close attention to the interpretability of the model. Furthermore, data augmentation techniques were involved during preprocessing, increasing the dataset samples through image processing alterations. The fine-tuned YOLOv8 model achieved remarkable results, with 0.9 accuracy, 0.85 precision, 0.85 recall, and 0.85 F1-score, computed by averaging the values among all the individual classes for each metric. This study shows the proposed architecture's effectiveness in enhancing the AO/OTA system's classification of femur fractures, assisting physicians in making prompt and accurate diagnoses.
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Affiliation(s)
- Giorgia Marullo
- Department of Management, Production, and Design, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino 10129, Italy
| | - Luca Ulrich
- Department of Management, Production, and Design, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino 10129, Italy
| | - Francesca Giada Antonaci
- Department of Management, Production, and Design, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino 10129, Italy
| | - Andrea Audisio
- Pediatric Orthopaedics and Traumatology, Regina Margherita Children's Hospital, Torino 10126, Italy
| | - Alessandro Aprato
- Department of Surgical Sciences, University of Turin, Torino 10124, Italy
| | - Alessandro Massè
- Department of Surgical Sciences, University of Turin, Torino 10124, Italy
| | - Enrico Vezzetti
- Department of Management, Production, and Design, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino 10129, Italy
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Franco PN, Maino C, Mariani I, Gandola DG, Sala D, Bologna M, Talei Franzesi C, Corso R, Ippolito D. Diagnostic performance of an AI algorithm for the detection of appendicular bone fractures in pediatric patients. Eur J Radiol 2024; 178:111637. [PMID: 39053306 DOI: 10.1016/j.ejrad.2024.111637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024]
Abstract
PURPOSE To evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm, previously trained using both adult and pediatric patients, for the detection of acute appendicular fractures in the pediatric population on conventional X-ray radiography (CXR). MATERIALS AND METHODS In this retrospective study, anonymized extremities CXRs of pediatric patients (age <17 years), with or without fractures, were included. Six hundred CXRs (maintaining the positive-for-fracture and negative-for-fracture balance) were included, grouping them per body part (shoulder/clavicle, elbow/upper arm, hand/wrist, leg/knee, foot/ankle). Follow-up CXRs and/or second-level imaging were considered as reference standard. A deep learning algorithm interpreted CXRs for fracture detection on a per-patient, per-radiograph, and per-location level, and its diagnostic performance values were compared with the reference standard. AI diagnostic performance was computed by using cross-tables, and 95 % confidence intervals [CIs] were obtained by bootstrapping. RESULTS The final cohort included 312 male and 288 female with a mean age of 8.9±4.5 years. Three undred CXRs (50 %) were positive for fractures, according to the reference standard. For all fractures, the AI tool showed a per-patient 91.3% (95%CIs = 87.6-94.3) sensitivity, 76.7% (71.5-81.3) specificity, and 84% (82.1-86.0) accuracy. In the per-radiograph analysis the AI tool showed 85% (81.9-87.8) sensitivity, 88.5% (86.3-90.4) specificity, and 87.2% (85.7-89.6) accuracy. In the per-location analysis, the AI tool identified 606 bounding boxes: 472 (77.9 %) were correct, 110 (18.1 %) incorrect, and 24 (4.0 %) were not-overlapping. CONCLUSION The AI algorithm provides good overall diagnostic performance for detecting appendicular fractures in pediatric patients.
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Affiliation(s)
- Paolo Niccolò Franco
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Cesare Maino
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy.
| | - Ilaria Mariani
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Davide Giacomo Gandola
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Davide Sala
- Synbrain The Human-Machine Cooperation - AI/ML solutions, Corso Milano 23, 20900 Monza, MB, Italy
| | - Marco Bologna
- Synbrain The Human-Machine Cooperation - AI/ML solutions, Corso Milano 23, 20900 Monza, MB, Italy
| | - Cammillo Talei Franzesi
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Rocco Corso
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Davide Ippolito
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy; Department of Medicine and Surgery - University of Milano Bicocca, Via Cadore 33, 20090 Monza, MB, Italy
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Kutbi M. Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review. Diagnostics (Basel) 2024; 14:1879. [PMID: 39272664 PMCID: PMC11394268 DOI: 10.3390/diagnostics14171879] [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: 07/15/2024] [Revised: 08/19/2024] [Accepted: 08/26/2024] [Indexed: 09/15/2024] Open
Abstract
Artificial intelligence (AI) is making notable advancements in the medical field, particularly in bone fracture detection. This systematic review compiles and assesses existing research on AI applications aimed at identifying bone fractures through medical imaging, encompassing studies from 2010 to 2023. It evaluates the performance of various AI models, such as convolutional neural networks (CNNs), in diagnosing bone fractures, highlighting their superior accuracy, sensitivity, and specificity compared to traditional diagnostic methods. Furthermore, the review explores the integration of advanced imaging techniques like 3D CT and MRI with AI algorithms, which has led to enhanced diagnostic accuracy and improved patient outcomes. The potential of Generative AI and Large Language Models (LLMs), such as OpenAI's GPT, to enhance diagnostic processes through synthetic data generation, comprehensive report creation, and clinical scenario simulation is also discussed. The review underscores the transformative impact of AI on diagnostic workflows and patient care, while also identifying research gaps and suggesting future research directions to enhance data quality, model robustness, and ethical considerations.
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Affiliation(s)
- Mohammed Kutbi
- College of Computing and Informatics, Saudi Electronic University, Riyadh 13316, Saudi Arabia
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Hembroff G, Klochko C, Craig J, Changarnkothapeecherikkal H, Loi RQ. Improved Automated Quality Control of Skeletal Wrist Radiographs Using Deep Multitask Learning. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01220-9. [PMID: 39187704 DOI: 10.1007/s10278-024-01220-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/17/2024] [Accepted: 07/29/2024] [Indexed: 08/28/2024]
Abstract
Radiographic quality control is an integral component of the radiology workflow. In this study, we developed a convolutional neural network model tailored for automated quality control, specifically designed to detect and classify key attributes of wrist radiographs including projection, laterality (based on the right/left marker), and the presence of hardware and/or casts. The model's primary objective was to ensure the congruence of results with image requisition metadata to pass the quality assessment. Using a dataset of 6283 wrist radiographs from 2591 patients, our multitask-capable deep learning model based on DenseNet 121 architecture achieved high accuracy in classifying projections (F1 Score of 97.23%), detecting casts (F1 Score of 97.70%), and identifying surgical hardware (F1 Score of 92.27%). The model's performance in laterality marker detection was lower (F1 Score of 82.52%), particularly for partially visible or cut-off markers. This paper presents a comprehensive evaluation of our model's performance, highlighting its strengths, limitations, and the challenges encountered during its development and implementation. Furthermore, we outline planned future research directions aimed at refining and expanding the model's capabilities for improved clinical utility and patient care in radiographic quality control.
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Affiliation(s)
- Guy Hembroff
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, 49931, USA.
| | - Chad Klochko
- Department of Radiology, Division of Musculoskeletal Radiology, Henry Ford Hospital, 2799 West Grand Boulevard, Detroit, MI, 48202, USA
| | - Joseph Craig
- Department of Radiology, Division of Musculoskeletal Radiology, Henry Ford Hospital, 2799 West Grand Boulevard, Detroit, MI, 48202, USA
| | | | - Richard Q Loi
- Department of Radiology, Division of Musculoskeletal Radiology, Henry Ford Hospital, 2799 West Grand Boulevard, Detroit, MI, 48202, USA
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Zhao T, Meng X, Wang Z, Hu Y, Fan H, Han J, Zhu N, Niu F. Diagnostic evaluation of blunt chest trauma by imaging-based application of artificial intelligence: A review. Am J Emerg Med 2024; 85:35-43. [PMID: 39213808 DOI: 10.1016/j.ajem.2024.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 08/12/2024] [Indexed: 09/04/2024] Open
Abstract
Artificial intelligence (AI) is becoming increasingly integral in clinical practice, such as during imaging tasks associated with the diagnosis and evaluation of blunt chest trauma (BCT). Due to significant advances in imaging-based deep learning, recent studies have demonstrated the efficacy of AI in the diagnosis of BCT, with a focus on rib fractures, pulmonary contusion, hemopneumothorax and others, demonstrating significant clinical progress. However, the complicated nature of BCT presents challenges in providing a comprehensive diagnosis and prognostic evaluation, and current deep learning research concentrates on specific clinical contexts, limiting its utility in addressing BCT intricacies. Here, we provide a review of the available evidence surrounding the potential utility of AI in BCT, and additionally identify the challenges impeding its development. This review offers insights on how to optimize the role of AI in the diagnostic evaluation of BCT, which can ultimately enhance patient care and outcomes in this critical clinical domain.
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Affiliation(s)
- Tingting Zhao
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China
| | - Xianghong Meng
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China.
| | - Zhi Wang
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China.
| | - Yongcheng Hu
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China
| | - Hongxing Fan
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Jun Han
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China
| | - Nana Zhu
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Feige Niu
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
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Nowroozi A, Salehi MA, Shobeiri P, Agahi S, Momtazmanesh S, Kaviani P, Kalra MK. Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis. Clin Radiol 2024; 79:579-588. [PMID: 38772766 DOI: 10.1016/j.crad.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/09/2024] [Accepted: 04/15/2024] [Indexed: 05/23/2024]
Abstract
PURPOSE Fracture detection is one of the most commonly used and studied aspects of artificial intelligence (AI) in medicine. In this systematic review and meta-analysis, we aimed to summarize available literature and data regarding AI performance in fracture detection on plain radiographs and various factors affecting it. METHODS We systematically reviewed studies evaluating AI algorithms in detecting bone fractures in plain radiographs, combined their performance using meta-analysis (a bivariate regression approach), and compared it with that of clinicians. We also analyzed the factors potentially affecting algorithm performance using meta-regression. RESULTS Our analysis included 100 studies. In 83 studies with confusion matrices, AI algorithms showed a sensitivity of 91.43% and a specificity of 92.12% (Area under the summary receiver operator curve = 0.968). After adjustment and false discovery rate correction, tibia/fibula (excluding ankle) fractures were associated with higher (7.0%, p=0.004) AI sensitivity, while more recent publications (5.5%, p=0.003) and Xception architecture (6.6%, p<0.001) were associated with higher specificity. Clinicians and AI showed similar specificity in fracture identification, although AI leaned to higher sensitivity (7.6%, p=0.07). Radiologists, on the other hand, were more specific than AI overall and in several subgroups, and more sensitive to hip fractures before FDR correction. CONCLUSIONS Currently available AI aids could result in a significant improvement in care where radiologists are not readily available. Moreover, identifying factors affecting algorithm performance could guide AI development teams in their process of optimizing their products.
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Affiliation(s)
- A Nowroozi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - M A Salehi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - P Shobeiri
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - S Agahi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - S Momtazmanesh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - P Kaviani
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - M K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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11
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Dasegowda G, Sato JY, Elton DC, Garza-Frias E, Schultz T, Bridge CP, Bizzo BC, Kalra MK, Dreyer KJ. No code machine learning: validating the approach on use-case for classifying clavicle fractures. Clin Imaging 2024; 112:110207. [PMID: 38838448 DOI: 10.1016/j.clinimag.2024.110207] [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: 04/24/2024] [Accepted: 05/23/2024] [Indexed: 06/07/2024]
Abstract
PURPOSE We created an infrastructure for no code machine learning (NML) platform for non-programming physicians to create NML model. We tested the platform by creating an NML model for classifying radiographs for the presence and absence of clavicle fractures. METHODS Our IRB-approved retrospective study included 4135 clavicle radiographs from 2039 patients (mean age 52 ± 20 years, F:M 1022:1017) from 13 hospitals. Each patient had two-view clavicle radiographs with axial and anterior-posterior projections. The positive radiographs had either displaced or non-displaced clavicle fractures. We configured the NML platform to automatically retrieve the eligible exams using the series' unique identification from the hospital virtual network archive via web access to DICOM Objects. The platform trained a model until the validation loss plateaus. Once the testing was complete, the platform provided the receiver operating characteristics curve and confusion matrix for estimating sensitivity, specificity, and accuracy. RESULTS The NML platform successfully retrieved 3917 radiographs (3917/4135, 94.7 %) and parsed them for creating a ML classifier with 2151 radiographs in the training, 100 radiographs for validation, and 1666 radiographs in testing datasets (772 radiographs with clavicle fracture, 894 without clavicle fracture). The network identified clavicle fracture with 90 % sensitivity, 87 % specificity, and 88 % accuracy with AUC of 0.95 (confidence interval 0.94-0.96). CONCLUSION A NML platform can help physicians create and test machine learning models from multicenter imaging datasets such as the one in our study for classifying radiographs based on the presence of clavicle fracture.
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Affiliation(s)
- Giridhar Dasegowda
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA
| | - James Yuichi Sato
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA
| | - Daniel C Elton
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA
| | - Emiliano Garza-Frias
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA
| | - Thomas Schultz
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA
| | - Christopher P Bridge
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA
| | - Bernardo C Bizzo
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA.
| | - Keith J Dreyer
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA
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12
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Petrella RJ. The AI Future of Emergency Medicine. Ann Emerg Med 2024; 84:139-153. [PMID: 38795081 DOI: 10.1016/j.annemergmed.2024.01.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 05/27/2024]
Abstract
In the coming years, artificial intelligence (AI) and machine learning will likely give rise to profound changes in the field of emergency medicine, and medicine more broadly. This article discusses these anticipated changes in terms of 3 overlapping yet distinct stages of AI development. It reviews some fundamental concepts in AI and explores their relation to clinical practice, with a focus on emergency medicine. In addition, it describes some of the applications of AI in disease diagnosis, prognosis, and treatment, as well as some of the practical issues that they raise, the barriers to their implementation, and some of the legal and regulatory challenges they create.
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Affiliation(s)
- Robert J Petrella
- Emergency Departments, CharterCARE Health Partners, Providence and North Providence, RI; Emergency Department, Boston VA Medical Center, Boston, MA; Emergency Departments, Steward Health Care System, Boston and Methuen, MA; Harvard Medical School, Boston, MA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA; Department of Medicine, Brigham and Women's Hospital, Boston, MA.
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13
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Zhu X, Liu D, Liu L, Guo J, Li Z, Zhao Y, Wu T, Liu K, Liu X, Pan X, Qi L, Zhang Y, Cheng L, Chen B. Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study. Orthop Surg 2024; 16:2052-2065. [PMID: 38952050 PMCID: PMC11293932 DOI: 10.1111/os.14155] [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: 03/11/2024] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 07/03/2024] Open
Abstract
BACKGROUND The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT). OBJECTIVE To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture. METHODS CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models. RESULTS The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer-Lemeshow goodness-of-fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p-value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly. CONCLUSION T_P1NT was an independent risk factor of refracture. Our 3D-FULL model showed better performance in predicting high-risk population of spine refracture than other models and junior doctors do. This model can be applicable to real-world translation due to its automatic segmentation and detection.
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Affiliation(s)
- Xuetao Zhu
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Dejian Liu
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Lian Liu
- Department of Emergency SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Jingxuan Guo
- Department of anesthesiologyAffiliated Hospital of Shandong University of Traditional Chinese MedicineJinanChina
| | - Zedi Li
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Yixiang Zhao
- Department of Orthopaedic SurgeryYantaishan HospitalYantaiChina
| | - Tianhao Wu
- Department of Hepatopancreatobiliary SurgeryGraduate School of Dalian Medical UniversityDalianChina
| | - Kaiwen Liu
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Xinyu Liu
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Xin Pan
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Lei Qi
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Yuanqiang Zhang
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Lei Cheng
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Bin Chen
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
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Oude Nijhuis KD, Dankelman LHM, Wiersma JP, Barvelink B, IJpma FFA, Verhofstad MHJ, Doornberg JN, Colaris JW, Wijffels MME. AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review. Eur J Trauma Emerg Surg 2024:10.1007/s00068-024-02557-0. [PMID: 38981869 DOI: 10.1007/s00068-024-02557-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/14/2024] [Indexed: 07/11/2024]
Abstract
PURPOSE Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools' accuracy and reliability remain challenging. Artificial intelligence (AI), particularly Convolutional Neural Networks (CNNs), can evaluate radiographic images with high performance. This systematic review aims to summarize studies utilizing CNNs to detect, classify, or predict loss of threshold alignment of DRFs. METHODS A literature search was performed according to the PRISMA. Studies were eligible when the use of AI for the detection, classification, or prediction of loss of threshold alignment was analyzed. Quality assessment was done with a modified version of the methodologic index for non-randomized studies (MINORS). RESULTS Of the 576 identified studies, 15 were included. On fracture detection, studies reported sensitivity and specificity ranging from 80 to 99% and 73-100%, respectively; the AUC ranged from 0.87 to 0.99; the accuracy varied from 82 to 99%. The accuracy of fracture classification ranged from 60 to 81% and the AUC from 0.59 to 0.84. No studies focused on predicting loss of thresholds alignement of DRFs. CONCLUSION AI models for DRF detection show promising performance, indicating the potential of algorithms to assist clinicians in the assessment of radiographs. In addition, AI models showed similar performance compared to clinicians. No algorithms for predicting the loss of threshold alignment were identified in our literature search despite the clinical relevance of such algorithms.
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Affiliation(s)
- Koen D Oude Nijhuis
- Department of Orthopedic Surgery, Groningen, Groningen University Medical Centre, Groningen, The Netherlands.
- Department of Surgery, Groningen, University Medical Centre, Groningen, The Netherlands.
| | - Lente H M Dankelman
- Trauma Research Unit Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands.
- Department of Orthopedic Surgery, Hand and Arm Center, Massachusetts General Hospital, Boston MA, Harvard Medical School, Boston MA, The Netherlands.
| | - Jort P Wiersma
- Department of Orthopedic Surgery, Groningen, Groningen University Medical Centre, Groningen, The Netherlands
- University Medical Center, Utrecht, The Netherlands
| | - Britt Barvelink
- Department of Orthopedics and Sports Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Frank F A IJpma
- Department of Surgery, Groningen, University Medical Centre, Groningen, The Netherlands
| | - Michael H J Verhofstad
- Trauma Research Unit Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands
| | - Job N Doornberg
- Department of Orthopedic Surgery, Groningen, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen, University Medical Centre, Groningen, The Netherlands
- Department of Orthopaedic and Trauma Surgery, Flinders University and Flinders Medical Centre, Adelaide, Australia
| | - Joost W Colaris
- Department of Orthopedics and Sports Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Mathieu M E Wijffels
- Trauma Research Unit Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands
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15
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Hsiung W, Lin HY, Lin HH, Yao YC, Wang ST, Chang MC, Chou PH. MRI-based lesion quality score assessing ossification of the posterior longitudinal ligament of the cervical spine. Spine J 2024; 24:1162-1169. [PMID: 38365006 DOI: 10.1016/j.spinee.2024.02.007] [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: 11/03/2023] [Revised: 01/14/2024] [Accepted: 02/06/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND CONTEXT No method currently exists for MRI-based determination of ossification of the posterior longitudinal ligament (OPLL) of the cervical spine using objective criteria. PURPOSE The purpose of this study was to develop an MRI-based score to determine whether a lesion represents a cervical OPLL lesion and to establish the objective diagnostic value. STUDY DESIGN Retrospective cohort in a single medical institution. PATIENT SAMPLE Thirty-five patients undergoing surgery for OPLL (Group A) and 99 patients undergoing cervical disc arthroplasty for soft disc herniation (Group B) between 2011 and 2020 were retrospectively included. All OPLL lesions on unenhanced MRI scan were correlated with a corresponding CT scan. Demographics were comparable between the two groups. OUTCOME MEASURES (PHYSIOLOGIC MEASURES) Using unenhanced magnetic resonance imaging (MRI), the T1- and T2- lesion quality (LQ) scores were calculated. Receiver operating characteristic (ROC) analysis was performed to calculate the area-under-the-curve (AUC) of both LQ scores as a predictor of the presence of OPLL. Computed tomography (CT)-based Hounsfield unit (HU) values of OPLL lesions were obtained and compared with both LQ scores. The LQ scores for MRI scanners from different manufacturers were compared using Student's t test to confirm the validity of the LQ score by scanner type. METHODS The regions of interest for signal intensity (SI) were defined as the darkest site of the lesion and the cerebrospinal fluid (CSF) at the cerebellomedullary cistern. The T1 and T2 LQ scores were measured as the ratio of the SI at the darkest site of the lesion divided by the SI of the CSF. RESULTS The T1 and T2 LQ scores in Group A were significantly lower than those in Group B (p<.001). ROC analysis determined that T1 and T2 LQ scores of 0.46 and 0.07, respectively, could distinguish the presence of OPLL with an accuracy of 0.93 and 0.89, respectively (p<.001). When the T1 LQ score of the lesion is <0.46, a diagnosis of OPLL may be suspected with 100% sensitivity and 92.3% specificity. The HU of the lesion had a moderate negative correlation with the T1 LQ score (r=-0.665, p<.0001). Both LQ scores were unaffected by manufacturer type. CONCLUSIONS This study found a correlation between the MRI-based T1 LQ scores and CT-based HU value for identifying OPLL lesions. Additional studies will be needed to validate that the T1 LQ score from the unenhanced MRI scan can identify cervical OPLL.
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Affiliation(s)
- Wei Hsiung
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Orthopedic Surgery, Shin Kong Wu Huo-Shih Memorial Hospital
| | - Han-Ying Lin
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Hsi-Hsien Lin
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Cheng Yao
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Shih-Tien Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan; Kinmen Hospital, Ministry of Health and Welfare, Taiwan
| | - Ming-Chau Chang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Po-Hsin Chou
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan.
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16
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Gan K, Liu Y, Zhang T, Xu D, Lian L, Luo Z, Li J, Lu L. Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01144-4. [PMID: 38862852 DOI: 10.1007/s10278-024-01144-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 06/13/2024]
Abstract
Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to construct a model for the automatic segmentation of wrist radiographs using a deep learning approach and further perform automatic identification and classification of DRF. A total of 2240 participants with anteroposterior wrist radiographs from one hospital between January 2015 and October 2021 were included. The outcomes were automatic segmentation of wrist radiographs, identification of DRF, and classification of DRF (type A, type B, type C). The Unet model and Fast-RCNN model were used for automatic segmentation. The DenseNet121 model and ResNet50 model were applied to DRF identification of DRF. The DenseNet121 model, ResNet50 model, VGG-19 model, and InceptionV3 model were used for DRF classification. The area under the curve (AUC) with 95% confidence interval (CI), accuracy, precision, and F1-score was utilized to assess the effectiveness of the identification and classification models. Of these 2240 participants, 1440 (64.3%) had DRF, of which 701 (48.7%) were type A, 278 (19.3%) were type B, and 461 (32.0%) were type C. Both the Unet model and the Fast-RCNN model showed good segmentation of wrist radiographs. For DRF identification, the AUCs of the DenseNet121 model and the ResNet50 model in the testing set were 0.941 (95%CI: 0.926-0.965) and 0.936 (95%CI: 0.913-0.955), respectively. The AUCs of the DenseNet121 model (testing set) for classification type A, type B, and type C were 0.96, 0.96, and 0.96, respectively. The DenseNet121 model may provide clinicians with a tool for interpreting wrist radiographs.
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Affiliation(s)
- Kaifeng Gan
- Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China
| | - Yunpeng Liu
- Ningbo University of Technology, Ningbo, 315100, Zhejiang, China
| | - Ting Zhang
- Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China
| | - Dingli Xu
- Health Science Center, Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Leidong Lian
- Health Science Center, Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Zhe Luo
- Health Science Center, Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Jin Li
- Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China
| | - Liangjie Lu
- Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China.
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Talebi S, Gai S, Sossin A, Zhu V, Tong E, Mofrad MRK. Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics. Ann Biomed Eng 2024; 52:1568-1575. [PMID: 38402314 PMCID: PMC11082011 DOI: 10.1007/s10439-024-03471-7] [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: 10/27/2023] [Accepted: 02/06/2024] [Indexed: 02/26/2024]
Abstract
Dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) is a non-invasive imaging technique for hemodynamic measurements. Various perfusion parameters, such as cerebral blood volume (CBV) and cerebral blood flow (CBF), can be derived from DSC-MRP, hence this non-invasive imaging protocol is widely used clinically for the diagnosis and assessment of intracranial pathologies. Currently, most institutions use commercially available software to compute the perfusion parametric maps. However, these conventional methods often have limitations, such as being time-consuming and sensitive to user input, which can lead to inconsistent results; this highlights the need for a more robust and efficient approach like deep learning. Using the relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) perfusion maps generated by FDA-approved software, we trained a multistage deep learning model. The model, featuring a combination of a 1D convolutional neural network (CNN) and a 2D U-Net encoder-decoder network, processes each 4D MRP dataset by integrating temporal and spatial features of the brain for voxel-wise perfusion parameters prediction. An auxiliary model, with similar architecture, but trained with truncated datasets that had fewer time-points, was designed to explore the contribution of temporal features. Both qualitatively and quantitatively evaluated, deep learning-generated rCBV and rCBF maps showcased effective integration of temporal and spatial data, producing comprehensive predictions for the entire brain volume. Our deep learning model provides a robust and efficient approach for calculating perfusion parameters, demonstrating comparable performance to FDA-approved commercial software, and potentially mitigating the challenges inherent to traditional techniques.
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Affiliation(s)
- Salmonn Talebi
- Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA
| | - Siyu Gai
- Departments of Electrical Engineering and Computer Science, University of California, Berkeley, California, USA
| | - Aaron Sossin
- Department of Bioinformatics, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Vivian Zhu
- Department of Bioinformatics, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Elizabeth Tong
- Department of Radiology, Stanford School of Medicine, Stanford University, 725 Welch Rd Rm 1860, Palo Alto, Stanford, CA, 94304, USA.
| | - Mohammad R K Mofrad
- Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA.
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Pridgen B, von Rabenau L, Luan A, Gu AJ, Wang DS, Langlotz C, Chang J, Do B. Automatic Detection of Perilunate and Lunate Dislocations on Wrist Radiographs Using Deep Learning. Plast Reconstr Surg 2024; 153:1138e-1141e. [PMID: 37467052 DOI: 10.1097/prs.0000000000010928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
SUMMARY Delayed or missed diagnosis of perilunate or lunate dislocations can lead to significant morbidity. Advances in computer vision provide an opportunity to improve diagnostic performance. In this study, a deep learning algorithm was used for detection of perilunate and lunate dislocations on lateral wrist radiographs. A total of 435 lateral wrist radiographs were labeled as normal or pathologic (perilunate or lunate dislocation). The lunate in each radiograph was segmented with a rectangular bounding box. Images were partitioned into training and test sets. Two neural networks, consisting of an object detector followed by an image classifier, were applied in series. First, the object detection module was used to localize the lunate. Next, the image classifier performed a binary classification for normal or pathologic. The accuracy, sensitivity, and specificity of the overall system were evaluated. A receiver operating characteristic curve and the associated area under the curve were used to demonstrate the overall performance of the computer vision algorithm. The lunate object detector was 97.0% accurate at identifying the lunate. Accuracy was 98.7% among the subgroup of normal wrist radiographs and 91.3% among the subgroup of wrist radiographs with perilunate/lunate dislocations. The perilunate/lunate dislocation classifier had a sensitivity (recall) of 93.8%, a specificity of 93.3%, and an accuracy of 93.4%. The area under the curve was 0.986. The authors have developed a proof-of-concept computer vision system for diagnosis of perilunate/lunate dislocations on lateral wrist radiographs. This novel deep learning algorithm has potential to improve clinical sensitivity to ultimately prevent delayed or missed diagnosis of these injuries.
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Affiliation(s)
- Brian Pridgen
- From the Division of Plastic Surgery, Department of Surgery
- The Buncke Clinic
| | | | - Anna Luan
- From the Division of Plastic Surgery, Department of Surgery
| | | | - David S Wang
- Department of Radiology, Stanford University School of Medicine
| | - Curtis Langlotz
- Department of Radiology, Stanford University School of Medicine
| | - James Chang
- From the Division of Plastic Surgery, Department of Surgery
| | - Bao Do
- Department of Radiology, Stanford University School of Medicine
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19
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Kim J, Seok J. ctGAN: combined transformation of gene expression and survival data with generative adversarial network. Brief Bioinform 2024; 25:bbae325. [PMID: 38980369 PMCID: PMC11232285 DOI: 10.1093/bib/bbae325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/29/2024] [Accepted: 06/21/2024] [Indexed: 07/10/2024] Open
Abstract
Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival analysis studies on diseases with high mortality rates, such as cancer, are indispensable. However, deep learning models are plagued by overfitting owing to the limited sample size relative to the large number of genes. Consequently, the latest style-transfer deep generative models have been implemented to generate gene expression data. However, these models are limited in their applicability for clinical purposes because they generate only transcriptomic data. Therefore, this study proposes ctGAN, which enables the combined transformation of gene expression and survival data using a generative adversarial network (GAN). ctGAN improves survival analysis by augmenting data through style transformations between breast cancer and 11 other cancer types. We evaluated the concordance index (C-index) enhancements compared with previous models to demonstrate its superiority. Performance improvements were observed in nine of the 11 cancer types. Moreover, ctGAN outperformed previous models in seven out of the 11 cancer types, with colon adenocarcinoma (COAD) exhibiting the most significant improvement (median C-index increase of ~15.70%). Furthermore, integrating the generated COAD enhanced the log-rank p-value (0.041) compared with using only the real COAD (p-value = 0.797). Based on the data distribution, we demonstrated that the model generated highly plausible data. In clustering evaluation, ctGAN exhibited the highest performance in most cases (89.62%). These findings suggest that ctGAN can be meaningfully utilized to predict disease progression and select personalized treatments in the medical field.
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Affiliation(s)
- Jaeyoon Kim
- School of Electrical and Computer Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
| | - Junhee Seok
- School of Electrical and Computer Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
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20
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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] [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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21
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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; 144:2461-2467. [PMID: 38578309 PMCID: PMC11093861 DOI: 10.1007/s00402-024-05298-2] [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: 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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22
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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 2024; 31:1989-1999. [PMID: 37993303 DOI: 10.1016/j.acra.2023.10.042] [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: 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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23
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Cheng CT, Kuo LW, Ouyang CH, Hsu CP, Lin WC, Fu CY, Kang SC, Liao CH. Development and evaluation of a deep learning-based model for simultaneous detection and localization of rib and clavicle fractures in trauma patients' chest radiographs. Trauma Surg Acute Care Open 2024; 9:e001300. [PMID: 38646620 PMCID: PMC11029226 DOI: 10.1136/tsaco-2023-001300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024] Open
Abstract
Purpose To develop a rib and clavicle fracture detection model for chest radiographs in trauma patients using a deep learning (DL) algorithm. Materials and methods We retrospectively collected 56 145 chest X-rays (CXRs) from trauma patients in a trauma center between August 2008 and December 2016. A rib/clavicle fracture detection DL algorithm was trained using this data set with 991 (1.8%) images labeled by experts with fracture site locations. The algorithm was tested on independently collected 300 CXRs in 2017. An external test set was also collected from hospitalized trauma patients in a regional hospital for evaluation. The receiver operating characteristic curve with area under the curve (AUC), accuracy, sensitivity, specificity, precision, and negative predictive value of the model on each test set was evaluated. The prediction probability on the images was visualized as heatmaps. Results The trained DL model achieved an AUC of 0.912 (95% CI 87.8 to 94.7) on the independent test set. The accuracy, sensitivity, and specificity on the given cut-off value are 83.7, 86.8, and 80.4, respectively. On the external test set, the model had a sensitivity of 88.0 and an accuracy of 72.5. While the model exhibited a slight decrease in accuracy on the external test set, it maintained its sensitivity in detecting fractures. Conclusion The algorithm detects rib and clavicle fractures concomitantly in the CXR of trauma patients with high accuracy in locating lesions through heatmap visualization.
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Affiliation(s)
- Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Ling-Wei Kuo
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Chun-Hsiang Ouyang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Chi-Po Hsu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Wei-Cheng Lin
- Department of Electrical Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Chih-Yuan Fu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Shih-Ching Kang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
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24
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Burkow J, Holste G, Otjen J, Perez F, Junewick J, Zbojniewicz A, Romberg E, Menashe S, Frost J, Alessio A. High sensitivity methods for automated rib fracture detection in pediatric radiographs. Sci Rep 2024; 14:8372. [PMID: 38600311 PMCID: PMC11006902 DOI: 10.1038/s41598-024-59077-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 04/07/2024] [Indexed: 04/12/2024] Open
Abstract
Rib fractures are highly predictive of non-accidental trauma in children under 3 years old. Rib fracture detection in pediatric radiographs is challenging because fractures can be obliquely oriented to the imaging detector, obfuscated by other structures, incomplete, and non-displaced. Prior studies have shown up to two-thirds of rib fractures may be missed during initial interpretation. In this paper, we implemented methods for improving the sensitivity (i.e. recall) performance for detecting and localizing rib fractures in pediatric chest radiographs to help augment performance of radiology interpretation. These methods adapted two convolutional neural network (CNN) architectures, RetinaNet and YOLOv5, and our previously proposed decision scheme, "avalanche decision", that dynamically reduces the acceptance threshold for proposed regions in each image. Additionally, we present contributions of using multiple image pre-processing and model ensembling techniques. Using a custom dataset of 1109 pediatric chest radiographs manually labeled by seven pediatric radiologists, we performed 10-fold cross-validation and reported detection performance using several metrics, including F2 score which summarizes precision and recall for high-sensitivity tasks. Our best performing model used three ensembled YOLOv5 models with varied input processing and an avalanche decision scheme, achieving an F2 score of 0.725 ± 0.012. Expert inter-reader performance yielded an F2 score of 0.732. Results demonstrate that our combination of sensitivity-driving methods provides object detector performance approaching the capabilities of expert human readers, suggesting that these methods may provide a viable approach to identify all rib fractures.
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Affiliation(s)
| | | | | | | | - Joseph Junewick
- Michigan State University, East Lansing, MI, 48823, USA
- Helen DeVos Children's Hospital, Grand Rapids, MI, USA
- Advanced Radiology Services, Grand Rapids, MI, USA
| | - Andy Zbojniewicz
- Michigan State University, East Lansing, MI, 48823, USA
- Helen DeVos Children's Hospital, Grand Rapids, MI, USA
- Advanced Radiology Services, Grand Rapids, MI, USA
| | | | | | - Jamie Frost
- Michigan State University, East Lansing, MI, 48823, USA
- Helen DeVos Children's Hospital, Grand Rapids, MI, USA
- Advanced Radiology Services, Grand Rapids, MI, USA
| | - Adam Alessio
- Michigan State University, East Lansing, MI, 48823, USA.
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25
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Dadon Z, Rav Acha M, Orlev A, Carasso S, Glikson M, Gottlieb S, Alpert EA. Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction. Diagnostics (Basel) 2024; 14:767. [PMID: 38611680 PMCID: PMC11011323 DOI: 10.3390/diagnostics14070767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 03/28/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024] Open
Abstract
INTRODUCTION Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. AIM To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. METHODS Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. RESULTS The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083-6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. CONCLUSION AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.
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Affiliation(s)
- Ziv Dadon
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Moshe Rav Acha
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Amir Orlev
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shemy Carasso
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Michael Glikson
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shmuel Gottlieb
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Evan Avraham Alpert
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
- Department of Emergency Medicine, Hadassah Medical Center—Ein Kerem, Jerusalem 9112001, Israel
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26
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Lakkimsetti M, Devella SG, Patel KB, Dhandibhotla S, Kaur J, Mathew M, Kataria J, Nallani M, Farwa UE, Patel T, Egbujo UC, Meenashi Sundaram D, Kenawy S, Roy M, Khan SF. Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature. Cureus 2024; 16:e58400. [PMID: 38756258 PMCID: PMC11098056 DOI: 10.7759/cureus.58400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.
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Affiliation(s)
| | - Swati G Devella
- Medicine, Kempegowda Institute of Medical Sciences, Bangalore, IND
| | - Keval B Patel
- Surgery, Narendra Modi Medical College, Ahmedabad, IND
| | | | | | - Midhun Mathew
- Internal Medicine, Trinitas Regional Medical Center, Elizabeth, USA
| | | | - Manisha Nallani
- Medicine, Kamineni Academy of Medical Sciences and Research Center, Hyderabad, IND
| | - Umm E Farwa
- Emergency Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Tirath Patel
- Medicine, American University of Antigua, Saint John's, ATG
| | | | - Dakshin Meenashi Sundaram
- Internal Medicine, Employees' State Insurance Corporation (ESIC) Medical College & Post Graduate Institute of Medical Science and Research (PGIMSR), Chennai, IND
| | | | - Mehak Roy
- Internal Medicine, School of Medicine Science and Research, Delhi, IND
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27
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Wernér K, Anttila T, Hulkkonen S, Viljakka T, Haapamäki V, Ryhänen J. Detecting Avascular Necrosis of the Lunate from Radiographs Using a Deep-Learning Model. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:706-714. [PMID: 38343256 PMCID: PMC11031541 DOI: 10.1007/s10278-023-00964-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 11/04/2023] [Accepted: 11/07/2023] [Indexed: 04/20/2024]
Abstract
Deep-learning (DL) algorithms have the potential to change medical image classification and diagnostics in the coming decade. Delayed diagnosis and treatment of avascular necrosis (AVN) of the lunate may have a detrimental effect on patient hand function. The aim of this study was to use a segmentation-based DL model to diagnose AVN of the lunate from wrist postero-anterior radiographs. A total of 319 radiographs of the diseased lunate and 1228 control radiographs were gathered from Helsinki University Central Hospital database. Of these, 10% were separated to form a test set for model validation. MRI confirmed the absence of disease. In cases of AVN of the lunate, a hand surgeon at Helsinki University Hospital validated the accurate diagnosis using either MRI or radiography. For detection of AVN, the model had a sensitivity of 93.33% (95% confidence interval (CI) 77.93-99.18%), specificity of 93.28% (95% CI 87.18-97.05%), and accuracy of 93.28% (95% CI 87.99-96.73%). The area under the receiver operating characteristic curve was 0.94 (95% CI 0.88-0.99). Compared to three clinical experts, the DL model had better AUC than one clinical expert and only one expert had higher accuracy than the DL model. The results were otherwise similar between the model and clinical experts. Our DL model performed well and may be a future beneficial tool for screening of AVN of the lunate.
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Affiliation(s)
- Krista Wernér
- Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, 00290, Helsinki, Finland.
| | - Turkka Anttila
- Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, 00290, Helsinki, Finland
| | - Sina Hulkkonen
- Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, 00290, Helsinki, Finland
| | | | - Ville Haapamäki
- Department of Radiology, HUS Diagnostic Center, HUS Medical Imaging Center, Helsinki, Finland
| | - Jorma Ryhänen
- Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, 00290, Helsinki, Finland
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28
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Tieu A, Kroen E, Kadish Y, Liu Z, Patel N, Zhou A, Yilmaz A, Lee S, Deyer T. The Role of Artificial Intelligence in the Identification and Evaluation of Bone Fractures. Bioengineering (Basel) 2024; 11:338. [PMID: 38671760 PMCID: PMC11047896 DOI: 10.3390/bioengineering11040338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/23/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Artificial intelligence (AI), particularly deep learning, has made enormous strides in medical imaging analysis. In the field of musculoskeletal radiology, deep-learning models are actively being developed for the identification and evaluation of bone fractures. These methods provide numerous benefits to radiologists such as increased diagnostic accuracy and efficiency while also achieving standalone performances comparable or superior to clinician readers. Various algorithms are already commercially available for integration into clinical workflows, with the potential to improve healthcare delivery and shape the future practice of radiology. In this systematic review, we explore the performance of current AI methods in the identification and evaluation of fractures, particularly those in the ankle, wrist, hip, and ribs. We also discuss current commercially available products for fracture detection and provide an overview of the current limitations of this technology and future directions of the field.
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Affiliation(s)
- Andrew Tieu
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ezriel Kroen
- New York Medical College, Valhalla, NY 10595, USA
| | | | - Zelong Liu
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nikhil Patel
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander Zhou
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | | | - Timothy Deyer
- East River Medical Imaging, New York, NY 10021, USA
- Department of Radiology, Cornell Medicine, New York, NY 10021, USA
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Alzubaidi L, Salhi A, A.Fadhel M, Bai J, Hollman F, Italia K, Pareyon R, Albahri AS, Ouyang C, Santamaría J, Cutbush K, Gupta A, Abbosh A, Gu Y. Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images. PLoS One 2024; 19:e0299545. [PMID: 38466693 PMCID: PMC10927121 DOI: 10.1371/journal.pone.0299545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/12/2024] [Indexed: 03/13/2024] Open
Abstract
Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.
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Affiliation(s)
- Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
| | - Asma Salhi
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
| | | | - Jinshuai Bai
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
| | - Freek Hollman
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
| | - Kristine Italia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
| | - Roberto Pareyon
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
| | - A. S. Albahri
- Technical College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
| | - Chun Ouyang
- School of Information Systems, Queensland University of Technology, Brisbane, QLD, Australia
| | - Jose Santamaría
- Department of Computer Science, University of Jaén, Jaén, Spain
| | - Kenneth Cutbush
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- School of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Ashish Gupta
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
- Greenslopes Private Hospital, Brisbane, QLD, Australia
| | - Amin Abbosh
- School of Information Technology and Electrical Engineering, Brisbane, QLD, Australia
| | - Yuantong Gu
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
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Gitto S, Serpi F, Albano D, Risoleo G, Fusco S, Messina C, Sconfienza LM. AI applications in musculoskeletal imaging: a narrative review. Eur Radiol Exp 2024; 8:22. [PMID: 38355767 PMCID: PMC10866817 DOI: 10.1186/s41747-024-00422-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/29/2023] [Indexed: 02/16/2024] Open
Abstract
This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice. Key points • AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance.
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Affiliation(s)
- Salvatore Gitto
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Francesca Serpi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milan, Italy
| | - Giovanni Risoleo
- Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, Milan, Italy
| | - Stefano Fusco
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
| | - Carmelo Messina
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Luca Maria Sconfienza
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy.
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
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Xie Y, Li X, Chen F, Wen R, Jing Y, Liu C, Wang J. Artificial intelligence diagnostic model for multi-site fracture X-ray images of extremities based on deep convolutional neural networks. Quant Imaging Med Surg 2024; 14:1930-1943. [PMID: 38415122 PMCID: PMC10895109 DOI: 10.21037/qims-23-878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/24/2023] [Indexed: 02/29/2024]
Abstract
Background The rapid and accurate diagnosis of fractures is crucial for timely treatment of trauma patients. Deep learning, one of the most widely used forms of artificial intelligence (AI), is now commonly employed in medical imaging for fracture detection. This study aimed to construct a deep learning model using big data to recognize multiple-fracture X-ray images of extremity bones. Methods Radiographic imaging data of extremities were retrospectively collected from five hospitals between January 2017 and September 2020. The total number of people finally included was 25,635 and the total number of images included was 26,098. After labeling the lesions, the randomized method used 90% of the data as the training set to develop the fracture detection model, and the remaining 10% was used as the validation set to verify the model. The faster region convolutional neural networks (R-CNN) algorithm was adopted to construct diagnostic models for detection. The Dice coefficient was used to evaluate the image segmentation accuracy. The performances of detection models were evaluated with sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results The free-response receiver operating characteristic (FROC) curve value was 0.886 and 0.843 for the detection of single and multiple fractures, respectively. Additionally, the effective identification AUC for all parts was higher than 0.920. Notably, the AUC for wrist fractures reached 0.952. The average accuracy in detecting bone fracture regions in the extremities was 0.865. When analyzing single and multiple lesions at the patient level, the sensitivity was 0.957 for patients with multiple lesions and 0.852 for those with single lesions. In the segmentation task, the training set (the data set used by the machine learning model to train and learn) and the validation set (the data set used to evaluate the performance of the model) reached 0.996 and 0.975, respectively. Conclusions The faster R-CNN training algorithm exhibits excellent performance in simultaneously identifying fractures in the hands, feet, wrists, ankles, radius and ulna, and tibia and fibula on X-ray images. It demonstrates high accuracy, low false-negative rates, and controllable false-positive rates. It can serve as a valuable screening tool.
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Affiliation(s)
- Yanling Xie
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Xiaoming Li
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Fengxi Chen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Ru Wen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Yang Jing
- Huiying Medical Technology Co., Ltd., Beijing, China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
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Zhan H, Teng F, Liu Z, Yi Z, He J, Chen Y, Geng B, Xia Y, Wu M, Jiang J. Artificial Intelligence Aids Detection of Rotator Cuff Pathology: A Systematic Review. Arthroscopy 2024; 40:567-578. [PMID: 37355191 DOI: 10.1016/j.arthro.2023.06.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 05/28/2023] [Accepted: 06/01/2023] [Indexed: 06/26/2023]
Abstract
PURPOSE To determine the model performance of artificial intelligence (AI) in detecting rotator cuff pathology using different imaging modalities and to compare capability with physicians in clinical scenarios. METHODS The review followed the PRISMA guidelines and was registered on PROSPERO. The criteria were as follows: 1) studies on the application of AI in detecting rotator cuff pathology using medical images, and 2) studies on smart devices for assisting in diagnosis were excluded. The following data were extracted and recorded: statistical characteristics, input features, AI algorithms used, sample sizes of training and testing sets, and model performance. The data extracted from the included studies were narratively reviewed. RESULTS A total of 14 articles, comprising 23,119 patients, met the inclusion and exclusion criteria. The pooled mean age of the patients was 56.7 years, and the female rate was 56.1%. The area under the curve (AUC) of the algorithmic model to detect rotator cuff pathology from ultrasound images, MRI images, and radiographic series ranged from 0.789 to 0.950, 0.844 to 0.943, and 0.820 to 0.830, respectively. Notably, 1 of the studies reported that AI models based on ultrasound images demonstrated a diagnostic performance similar to that of radiologists. Another comparative study demonstrated that AI models using MRI images exhibited greater accuracy and specificity compared to orthopedic surgeons in the diagnosis of rotator cuff pathology, albeit not in sensitivity. CONCLUSIONS The detection of rotator cuff pathology has been significantly aided by the exceptional performance of AI models. In particular, these models are equally adept as musculoskeletal radiologists in using ultrasound to diagnose rotator cuff pathology. Furthermore, AI models exhibit statistically superior levels of accuracy and specificity when using MRI to diagnose rotator cuff pathology, albeit with no marked difference in sensitivity, in comparison to orthopaedic surgeons. LEVEL OF EVIDENCE Level III, systematic review of Level III studies.
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Affiliation(s)
- Hongwei Zhan
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Fei Teng
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Zhongcheng Liu
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Zhi Yi
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Jinwen He
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Yi Chen
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Bin Geng
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Yayi Xia
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China.
| | - Meng Wu
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Jin Jiang
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
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Niu ZB, Jia SY, Xu HH. Automated graptolite identification at high taxonomic resolution using residual networks. iScience 2024; 27:108549. [PMID: 38213629 PMCID: PMC10783601 DOI: 10.1016/j.isci.2023.108549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/23/2023] [Accepted: 11/20/2023] [Indexed: 01/13/2024] Open
Abstract
Graptolites, fossils significant for evolutionary studies and shale gas exploration, are traditionally identified visually by taxonomists due to their intricate morphologies and preservation challenges. Artificial intelligence (AI) holds great promise for transforming such meticulous tasks. In this paper, we demonstrate that graptolites can be identified with taxonomist accuracy using a deep learning model. We construct the most sophisticated and largest professional single organisms image dataset to date, which is composed of >34,000 images of 113 graptolite species annotated at pixel-level resolution to train the model, develop, and evaluate deep learning networks to classify graptolites. The model's performance surpassed taxonomists in accuracy, time, and generalization, achieving 86% and 81% accuracy in identifying graptolite genus and species, respectively. This AI-based method, capable of recognizing minute morphological details better than taxonomists, can be integrated into web and mobile apps, extending graptolite identification beyond research institutes and enhancing shale gas exploration efficiency.
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Affiliation(s)
- Zhi-Bin Niu
- College of Intelligence and Computing, Tianjin University, Tianjin 300354, China
- State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology and Centre for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Nanjing 210008, China
| | - Si-Yuan Jia
- College of Intelligence and Computing, Tianjin University, Tianjin 300354, China
| | - Hong-He Xu
- State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology and Centre for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Nanjing 210008, China
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Tsai DJ, Lin C, Lin CS, Lee CC, Wang CH, Fang WH. Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk. J Med Syst 2024; 48:12. [PMID: 38217829 DOI: 10.1007/s10916-023-02030-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024]
Abstract
A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.
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Affiliation(s)
- Dung-Jang Tsai
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan, R.O.C
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chin Lin
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
- School of Public Health, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chia-Cheng Lee
- Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chih-Hung Wang
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Wen-Hui Fang
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C..
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C..
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Pham TD, Holmes SB, Coulthard P. A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging. Front Artif Intell 2024; 6:1278529. [PMID: 38249794 PMCID: PMC10797131 DOI: 10.3389/frai.2023.1278529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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Liu CW, Chacon M, Crawford L, Polydore H, Ting T, Wilson NA. Machine Learning Improves the Accuracy of Trauma Team Activation Level Assignments in Pediatric Patients. J Pediatr Surg 2024; 59:74-79. [PMID: 37865573 PMCID: PMC10843072 DOI: 10.1016/j.jpedsurg.2023.09.014] [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: 08/14/2023] [Accepted: 09/06/2023] [Indexed: 10/23/2023]
Abstract
BACKGROUND The assignment of trauma team activation levels can be conceptualized as a classification task. Machine learning models can be used to optimize classification predictions. Our purpose was to demonstrate proof-of-concept for a machine learning tool for predicting trauma team activation levels in pediatric patients with traumatic injuries. METHODS Following IRB approval, we retrospectively collected data from the institutional trauma registry and electronic medical record at our Pediatric Trauma Center for all patients (age <18 y) who triggered a trauma team activation (1/2014-12/2021), including: demographics, mechanisms of injury, comorbidities, pre-hospital interventions, numeric variables, and the six "Need for Trauma Intervention (NFTI)" criteria. Three machine learning models (Logistic Regression, Random Forest, Support Vector Machine) were tested 1000 times in separate trials using the union of the Cribari and NFTI metrics as ground-truth (Injury Severity Score >15 or positive for any of 6 NFTI criteria = full activation). Model performance was quantified and compared to emergency department (ED) staff. RESULTS ED staff had 75% accuracy, an area under the curve (AUC) of 0.73 ± 0.04, and an F1 score of 0.49. The best performing of all machine learning models, the support vector machine, had 80% accuracy, AUC 0.81 ± 4.1e-5, F1 Score 0.80, with less variance compared to other models and ED staff. CONCLUSIONS All machine learning models outperformed ED staff in all performance metrics. These results suggest that data-driven methods can optimize trauma team activations in the ED, with potential improvements in both patient safety and hospital resource utilization. TYPE OF STUDY Economic/Decision Analysis or Modeling Studies. LEVEL OF EVIDENCE II.
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Affiliation(s)
- Catherine W Liu
- School of Medicine, University of Rochester, 601 Elmwood Avenue, Box 601A, Rochester NY, 14642, USA
| | - Miranda Chacon
- Department of Surgery, University of Rochester Medical Center, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA
| | - Loralai Crawford
- Department of Biomedical Engineering, University of Rochester, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA
| | - Hadassah Polydore
- Division of Pediatric Surgery, Department of Surgery, University of Rochester Medical Center, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA
| | - Tiffany Ting
- School of Medicine, University of Rochester, 601 Elmwood Avenue, Box 601A, Rochester NY, 14642, USA
| | - Nicole A Wilson
- School of Medicine, University of Rochester, 601 Elmwood Avenue, Box 601A, Rochester NY, 14642, USA; Department of Surgery, University of Rochester Medical Center, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA; Department of Biomedical Engineering, University of Rochester, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA; Division of Pediatric Surgery, Department of Surgery, University of Rochester Medical Center, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA.
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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 DIGITAL 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] [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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Zhong S, Yin X, Li X, Feng C, Gao Z, Liao X, Yang S, He S. Artificial intelligence applications in bone fractures: A bibliometric and science mapping analysis. Digit Health 2024; 10:20552076241279238. [PMID: 39257873 PMCID: PMC11384526 DOI: 10.1177/20552076241279238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 08/13/2024] [Indexed: 09/12/2024] Open
Abstract
Background Bone fractures are a common medical issue worldwide, causing a serious economic burden on society. In recent years, the application of artificial intelligence (AI) in the field of fracture has developed rapidly, especially in fracture diagnosis, where AI has shown significant capabilities comparable to those of professional orthopedic surgeons. This study aimed to review the development process and applications of AI in the field of fracture using bibliometric analysis, while analyzing the research hotspots and future trends in the field. Materials and methods Studies on AI and fracture were retrieved from the Web of Science Core Collections since 1990, a retrospective bibliometric and visualized study of the filtered data was conducted through CiteSpace and Bibliometrix R package. Results A total of 1063 publications were included in the analysis, with the annual publication rapidly growing since 2017. China had the most publications, and the United States had the most citations. Technical University of Munich, Germany, had the most publications. Doornberg JN was the most productive author. Most research in this field was published in Scientific Reports. Doi K's 2007 review in Computerized Medical Imaging and Graphics was the most influential paper. Conclusion AI application in fracture has achieved outstanding results and will continue to progress. In this study, we used a bibliometric analysis to assist researchers in understanding the basic knowledge structure, research hotspots, and future trends in this field, to further promote the development of AI applications in fracture.
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Affiliation(s)
- Sen Zhong
- Department of Orthopedic, Spinal Pain Research Institute, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaobing Yin
- Nursing Department, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaolan Li
- Fuzhou Medical College of Nanchang University, School of Stomatology, Fuzhou, China
| | - Chaobo Feng
- National Key Clinical Pain Medicine of China, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Zhiqiang Gao
- Department of Joint Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiang Liao
- National Key Clinical Pain Medicine of China, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Sheng Yang
- Department of Orthopedic, Spinal Pain Research Institute, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Shisheng He
- Department of Orthopedic, Spinal Pain Research Institute, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Bachmann R, Gunes G, Hangaard S, Nexmann A, Lisouski P, Boesen M, Lundemann M, Baginski SG. Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study. BJR Open 2024; 6:tzae011. [PMID: 38757067 PMCID: PMC11096271 DOI: 10.1093/bjro/tzae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/13/2023] [Accepted: 04/21/2024] [Indexed: 05/18/2024] Open
Abstract
Objectives The aim of this study was to evaluate the diagnostic performance of nonspecialist readers with and without the use of an artificial intelligence (AI) support tool to detect traumatic fractures on radiographs of the appendicular skeleton. Methods The design was a retrospective, fully crossed multi-reader, multi-case study on a balanced dataset of patients (≥2 years of age) with an AI tool as a diagnostic intervention. Fifteen readers assessed 340 radiographic exams, with and without the AI tool in 2 different sessions and the time spent was automatically recorded. Reference standard was established by 3 consultant radiologists. Sensitivity, specificity, and false positives per patient were calculated. Results Patient-wise sensitivity increased from 72% to 80% (P < .05) and patient-wise specificity increased from 81% to 85% (P < .05) in exams aided by the AI tool compared to the unaided exams. The increase in sensitivity resulted in a relative reduction of missed fractures of 29%. The average rate of false positives per patient decreased from 0.16 to 0.14, corresponding to a relative reduction of 21%. There was no significant difference in average reading time spent per exam. The largest gain in fracture detection performance, with AI support, across all readers, was on nonobvious fractures with a significant increase in sensitivity of 11 percentage points (pp) (60%-71%). Conclusions The diagnostic performance for detection of traumatic fractures on radiographs of the appendicular skeleton improved among nonspecialist readers tested AI fracture detection support tool showed an overall reader improvement in sensitivity and specificity when supported by an AI tool. Improvement was seen in both sensitivity and specificity without negatively affecting the interpretation time. Advances in knowledge The division and analysis of obvious and nonobvious fractures are novel in AI reader comparison studies like this.
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Affiliation(s)
| | | | - Stine Hangaard
- Department of Radiology, Herlev and Gentofte, Copenhagen University Hospital, Denmark
| | | | | | - Mikael Boesen
- Department of Radiology and Radiological AI Testcenter (RAIT) Denmark, Bispebjerg and Frederiksberg, Copenhagen University Hospital, Denmark
- Department of Clinical Medicine, Faculty of Health, and Medical Sciences, University of Copenhagen, Denmark
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Lee KH, Lee RW, Lee KH, Park W, Kwon SR, Lim MJ. The Development and Validation of an AI Diagnostic Model for Sacroiliitis: A Deep-Learning Approach. Diagnostics (Basel) 2023; 13:3643. [PMID: 38132228 PMCID: PMC10743277 DOI: 10.3390/diagnostics13243643] [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: 10/13/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023] Open
Abstract
PURPOSE Sacroiliitis refers to the inflammatory condition of the sacroiliac joints, frequently causing lower back pain. It is often associated with systemic conditions. However, its signs on radiographic images can be subtle, which may result in it being overlooked or underdiagnosed. This study aims to utilize artificial intelligence (AI) to create a diagnostic tool for more accurate sacroiliitis detection in radiological images, with the goal of optimizing treatment plans and improving patient outcomes. MATERIALS AND METHOD The study included 492 patients who visited our hospital. Right sacroiliac joint films were independently evaluated by two musculoskeletal radiologists using the Modified New York criteria (Normal, Grades 1-4). A consensus reading resolved disagreements. The images were preprocessed with Z-score standardization and histogram equalization. The DenseNet121 algorithm, a convolutional neural network with 201 layers, was used for learning and classification. All steps were performed on the DEEP:PHI platform. RESULT The AI model exhibited high accuracy across different grades: 94.53% (Grade 1), 95.83% (Grade 2), 98.44% (Grade 3), 96.88% (Grade 4), and 96.09% (Normal cases). Sensitivity peaked at Grade 3 and Normal cases (100%), while Grade 4 achieved perfect specificity (100%). PPVs ranged from 82.61% (Grade 1) to 100% (Grade 4), and NPVs peaked at 100% for Grade 3 and Normal cases. The F1 scores ranged from 64.41% (Grade 1) to 95.38% (Grade 3). CONCLUSIONS The AI diagnostic model showcased a robust performance in detecting and grading sacroiliitis, reflecting its potential to enhance diagnostic accuracy in clinical settings. By facilitating earlier and more accurate diagnoses, this model could substantially impact treatment strategies and patient outcomes.
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Affiliation(s)
- Kyu-Hong Lee
- Department of Radiology, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (K.-H.L.)
| | - Ro-Woon Lee
- Department of Radiology, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (K.-H.L.)
| | - Kyung-Hee Lee
- Department of Radiology, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (K.-H.L.)
| | - Won Park
- Division of Rheumatology, Department of Internal Medicine, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (W.P.)
| | - Seong-Ryul Kwon
- Division of Rheumatology, Department of Internal Medicine, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (W.P.)
| | - Mie-Jin Lim
- Division of Rheumatology, Department of Internal Medicine, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (W.P.)
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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: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [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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Oosterhoff JH, Jeon S, Akhbari B, Shin D, Tobert DG, Do S, Ashkani-Esfahani S. A deep learning approach using an ensemble model to autocreate an image-based hip fracture registry. OTA Int 2023; 6:e283. [PMID: 38152438 PMCID: PMC10750455 DOI: 10.1097/oi9.0000000000000283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 07/17/2023] [Indexed: 12/29/2023]
Abstract
Objectives With more than 300,000 patients per year in the United States alone, hip fractures are one of the most common injuries occurring in the elderly. The incidence is predicted to rise to 6 million cases per annum worldwide by 2050. Many fracture registries have been established, serving as tools for quality surveillance and evaluating patient outcomes. Most registries are based on billing and procedural codes, prone to under-reporting of cases. Deep learning (DL) is able to interpret radiographic images and assist in fracture detection; we propose to conduct a DL-based approach intended to autocreate a fracture registry, specifically for the hip fracture population. Methods Conventional radiographs (n = 18,834) from 2919 patients from Massachusetts General Brigham hospitals were extracted (images designated as hip radiographs within the medical record). We designed a cascade model consisting of 3 submodules for image view classification (MI), postoperative implant detection (MII), and proximal femoral fracture detection (MIII), including data augmentation and scaling, and convolutional neural networks for model development. An ensemble model of 10 models (based on ResNet, VGG, DenseNet, and EfficientNet architectures) was created to detect the presence of a fracture. Results The accuracy of the developed submodules reached 92%-100%; visual explanations of model predictions were generated through gradient-based methods. Time for the automated model-based fracture-labeling was 0.03 seconds/image, compared with an average of 12 seconds/image for human annotation as calculated in our preprocessing stages. Conclusion This semisupervised DL approach labeled hip fractures with high accuracy. This mitigates the burden of annotations in a large data set, which is time-consuming and prone to under-reporting. The DL approach may prove beneficial for future efforts to autocreate construct registries that outperform current diagnosis and procedural codes. Clinicians and researchers can use the developed DL approach for quality improvement, diagnostic and prognostic research purposes, and building clinical decision support tools.
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Affiliation(s)
- Jacobien H.F. Oosterhoff
- Department of Orthopaedic Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA
- Department of Engineering Systems and Services, Faculty Technology Policy Management, Delft University of Technology, Delft, the Netherlands
| | - Soomin Jeon
- Department of Orthopaedic Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA
- Department of Information Sciences and Mathematics, Dong-A University, Busan, South Korea
| | - Bardiya Akhbari
- Department of Orthopaedic Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - David Shin
- Department of Orthopaedic Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Daniel G. Tobert
- Department of Orthopaedic Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Synho Do
- Laboratory of Medical Imaging and Computation, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Soheil Ashkani-Esfahani
- Department of Orthopaedic Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA
- Foot & Ankle Research and Innovation Laboratory, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Tiwari A, Yadav AK, Akshay K, Bagaria V. Evaluation of machine learning models to identify hip arthroplasty implants using transfer learning algorithms. J Clin Orthop Trauma 2023; 47:102312. [PMID: 38196501 PMCID: PMC10772396 DOI: 10.1016/j.jcot.2023.102312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/06/2023] [Indexed: 01/11/2024] Open
Affiliation(s)
- Anjali Tiwari
- Department of Orthopedic Surgery, Sir H N Reliance Foundation Hospital, Girgaum, Mumbai, Maharashtra, India
| | - Amit Kumar Yadav
- International Training Fellow, Department of Trauma & Orthopedic Surgery, Wrightington Hospital, Wigan, UK
| | - K.S. Akshay
- Grant Government Medical College and Sir J J Group of Hospitals, India
| | - Vaibhav Bagaria
- Department of Orthopedic Surgery, Sir H N Reliance Foundation Hospital, Girgaum, Mumbai, Maharashtra, India
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Liao X, Yao C, Zhang J, Liu LZ. Recent advancement in integrating artificial intelligence and information technology with real-world data for clinical decision-making in China: A scoping review. J Evid Based Med 2023; 16:534-546. [PMID: 37772921 DOI: 10.1111/jebm.12549] [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: 05/24/2023] [Accepted: 08/31/2023] [Indexed: 09/30/2023]
Abstract
OBJECTIVE Striking innovations and advancements have been achieved with the use of artificial intelligence and healthcare information technology being integrated into clinical real-world data. The current scoping review aimed to provide an overview of the current status of artificial intelligence-/information technology-based clinical decision support tools in China. METHODS PubMed/MEDLINE, Embase, China National Knowledge Internet, and Wanfang data were searched for both English and Chinese literature. The gray literature search was conducted for commercially available tools. Original studies that focused on clinical decision support tools driven by artificial intelligence or information technology in China and were published between 2010 and February 2022 were included. Information extracted from each article was further synthesized by themes based on three types of clinical decision-making. RESULTS A total of 37 peer-reviewed publications and 13 commercially available tools were included in the final analysis. Among them, 32.0% were developed for disease diagnosis, 54.0% for risk prediction and classification, and 14.0% for disease management. Chronic diseases were the most popular therapeutic areas of exploration, with particular emphasis on cardiovascular and cerebrovascular diseases. Single-center electronic medical records were the mainstream data sources leveraged to inform clinical decision-making, with internal validation being predominately used for model evaluation. CONCLUSIONS To effectively promote the extensive use of real-world data and drive a paradigm shift in clinical decision-making in China, multidisciplinary collaboration of key stakeholders is urgently needed.
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Affiliation(s)
- Xiwen Liao
- Peking University Clinical Research Institute, Peking University First Hospital, Beijing, China
| | - Chen Yao
- Peking University Clinical Research Institute, Peking University First Hospital, Beijing, China
- Hainan Institute of Real World Data, Qionghai, Hainan, China
| | - Jun Zhang
- Center for Observational and Real-world Evidence (CORE), MSD R&D (China) Co., Ltd., Beijing, China
| | - Larry Z Liu
- Center for Observational and Real-world Evidence (CORE), Merck & Co Inc, Rahway, Rahway, New Jersey, USA
- Department of Population Health Sciences, Weill Cornell Medical College, New York City, New York, USA
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Ju RY, Cai W. Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm. Sci Rep 2023; 13:20077. [PMID: 37973984 PMCID: PMC10654405 DOI: 10.1038/s41598-023-47460-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeons, which requires time-consuming specialized training. With the rise of deep learning in the field of computer vision, network models applying for fracture detection has become an important research topic. In this paper, we use data augmentation to improve the model performance of YOLOv8 algorithm (the latest version of You Only Look Once) on a pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX), which is a public dataset. The experimental results show that our model has reached the state-of-the-art (SOTA) mean average precision (mAP 50). Specifically, mAP 50 of our model is 0.638, which is significantly higher than the 0.634 and 0.636 of the improved YOLOv7 and original YOLOv8 models. To enable surgeons to use our model for fracture detection on pediatric wrist trauma X-ray images, we have designed the application "Fracture Detection Using YOLOv8 App" to assist surgeons in diagnosing fractures, reducing the probability of error analysis, and providing more useful information for surgery.
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Affiliation(s)
- Rui-Yang Ju
- Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei City, 106335, Taiwan
| | - Weiming Cai
- Department of Hand and Foot Surgery, Jingjiang People's Hospital, Jingjiang City, 214500, China.
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Ibanez V, Jucker D, Ebert LC, Franckenberg S, Dobay A. Classification of rib fracture types from postmortem computed tomography images using deep learning. Forensic Sci Med Pathol 2023:10.1007/s12024-023-00751-x. [PMID: 37968549 DOI: 10.1007/s12024-023-00751-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/05/2023] [Indexed: 11/17/2023]
Abstract
Human or time resources can sometimes fall short in medical image diagnostics, and analyzing images in full detail can be a challenging task. With recent advances in artificial intelligence, an increasing number of systems have been developed to assist clinicians in their work. In this study, the objective was to train a model that can distinguish between various fracture types on different levels of hierarchical taxonomy and detect them on 2D-image representations of volumetric postmortem computed tomography (PMCT) data. We used a deep learning model based on the ResNet50 architecture that was pretrained on ImageNet data, and we used transfer learning to fine-tune it to our specific task. We trained our model to distinguish between "displaced," "nondisplaced," "ad latus," "ad longitudinem cum contractione," and "ad longitudinem cum distractione" fractures. Radiographs with no fractures were correctly predicted in 95-99% of cases. Nondisplaced fractures were correctly predicted in 80-86% of cases. Displaced fractures of the "ad latus" type were correctly predicted in 17-18% of cases. The other two displaced types of fractures, "ad longitudinem cum contractione" and "ad longitudinem cum distractione," were correctly predicted in 70-75% and 64-75% of cases, respectively. The model achieved the best performance when the level of hierarchical taxonomy was high, while it had more difficulties when the level of hierarchical taxonomy was lower. Overall, deep learning techniques constitute a reliable solution for forensic pathologists and medical practitioners seeking to reduce workload.
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Affiliation(s)
- Victor Ibanez
- Forensic Machine Learning Technology Center, Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Dario Jucker
- Zurich Institute of Forensic Medicine, 3D Centre Zurich, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Lars C Ebert
- Zurich Institute of Forensic Medicine, 3D Centre Zurich, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Sabine Franckenberg
- Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
- Zurich Institute of Forensic Medicine, 3D Centre Zurich, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Akos Dobay
- Forensic Machine Learning Technology Center, Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland.
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Li LT, Haley LC, Boyd AK, Bernstam EV. Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. J Biomed Inform 2023; 147:104531. [PMID: 37884177 DOI: 10.1016/j.jbi.2023.104531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/14/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023]
Abstract
INTRODUCTION The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic review, we aim to determine the main barriers to successful implementation of AI in healthcare and discuss potential ways to overcome these challenges. METHODS We conducted a literature search in PubMed (1/1/2001-1/1/2023). The search was restricted to publications in the English language, and human study subjects. We excluded articles that did not discuss AI, machine learning, predictive analytics, and barriers to the use of these techniques in health care. Using grounded theory methodology, we abstracted concepts to identify major barriers to AI use in medicine. RESULTS We identified a total of 2,382 articles. After reviewing the 306 included papers, we developed 19 major themes, which we categorized into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). These themes included: Lack of Explainability, Need for Validation Protocols, Need for Standards for Interoperability, Need for Reporting Guidelines, Need for Standardization of Performance Metrics, Lack of Plan for Updating Algorithm, Job Loss, Skills Loss, Workflow Challenges, Loss of Patient Autonomy and Consent, Disturbing the Patient-Clinician Relationship, Lack of Trust in AI, Logistical Challenges, Lack of strategic plan, Lack of Cost-effectiveness Analysis and Proof of Efficacy, Privacy, Liability, Bias and Social Justice, and Education. CONCLUSION We identified 19 major barriers to the use of AI in healthcare and categorized them into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). Future studies should expand on barriers in pediatric care and focus on developing clearly defined protocols to overcome these barriers.
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Affiliation(s)
- Linda T Li
- Department of Surgery, Division of Pediatric Surgery, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States; McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States.
| | - Lauren C Haley
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Alexandra K Boyd
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Elmer V Bernstam
- McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States; McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
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Aryasomayajula S, Hing CB, Siebachmeyer M, Naeini FB, Ejindu V, Leitch P, Gelfer Y, Zweiri Y. Developing an artificial intelligence diagnostic tool for paediatric distal radius fractures, a proof of concept study. Ann R Coll Surg Engl 2023; 105:721-728. [PMID: 37642151 PMCID: PMC10618045 DOI: 10.1308/rcsann.2023.0017] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Abstract
INTRODUCTION In the UK, 1 in 50 children sustain a fractured bone yearly, yet studies have shown that 34% of children sustaining an injury do not have a visible fracture on initial radiographs. Wrist fractures are particularly difficult to identify because the growth plate poses diagnostic challenges when interpreting radiographs. METHODS We developed Convolutional Neural Network (CNN) image recognition software to detect fractures in radiographs from children. A consecutive data set of 5,000 radiographs of the distal radius in children aged less than 19 years from 2014 to 2019 was used to train the CNN. In addition, transfer learning from a VGG16 CNN pretrained on non-radiological images was applied to improve generalisation of the network and the classification of radiographs. Hyperparameter tuning techniques were used to compare the model with the radiology reports that accompanied the original images to determine diagnostic test accuracy. RESULTS The training set consisted of 2,881 radiographs with a fracture and 1,571 without; 548 radiographs were outliers. With additional augmentation, the final data set consisted of 15,498 images. The data set was randomly split into three subsets: training (70%), validation (10%) and test (20%). After training for 20 epochs, the diagnostic test accuracy was 85%. DISCUSSION A CNN model is feasible in diagnosing paediatric wrist fractures. We demonstrated that this application could be utilised as a tool for improving diagnostic accuracy. Future work would involve developing automated treatment pathways for diagnosis, reducing unnecessary hospital visits and allowing staff redeployment to other areas.
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Affiliation(s)
| | - C B Hing
- St George's University Hospitals NHS Foundation Trust, UK
| | - M Siebachmeyer
- St George's University Hospitals NHS Foundation Trust, UK
| | | | - V Ejindu
- St George's University Hospitals NHS Foundation Trust, UK
| | - P Leitch
- St George's University London, UK
| | - Y Gelfer
- St George's University Hospitals NHS Foundation Trust, UK
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Kotsis F, Bächle H, Altenbuchinger M, Dönitz J, Njipouombe Nsangou YA, Meiselbach H, Kosch R, Salloch S, Bratan T, Zacharias HU, Schultheiss UT. Expectation of clinical decision support systems: a survey study among nephrologist end-users. BMC Med Inform Decis Mak 2023; 23:239. [PMID: 37884906 PMCID: PMC10605935 DOI: 10.1186/s12911-023-02317-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 09/29/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Chronic kidney disease (CKD), a major public health problem with differing disease etiologies, leads to complications, comorbidities, polypharmacy, and mortality. Monitoring disease progression and personalized treatment efforts are crucial for long-term patient outcomes. Physicians need to integrate different data levels, e.g., clinical parameters, biomarkers, and drug information, with medical knowledge. Clinical decision support systems (CDSS) can tackle these issues and improve patient management. Knowledge about the awareness and implementation of CDSS in Germany within the field of nephrology is scarce. PURPOSE Nephrologists' attitude towards any CDSS and potential CDSS features of interest, like adverse event prediction algorithms, is important for a successful implementation. This survey investigates nephrologists' experiences with and expectations towards a useful CDSS for daily medical routine in the outpatient setting. METHODS The 38-item questionnaire survey was conducted either by telephone or as a do-it-yourself online interview amongst nephrologists across all of Germany. Answers were collected and analysed using the Electronic Data Capture System REDCap, as well as Stata SE 15.1, and Excel. The survey consisted of four modules: experiences with CDSS (M1), expectations towards a helpful CDSS (M2), evaluation of adverse event prediction algorithms (M3), and ethical aspects of CDSS (M4). Descriptive statistical analyses of all questions were conducted. RESULTS The study population comprised 54 physicians, with a response rate of about 80-100% per question. Most participants were aged between 51-60 years (45.1%), 64% were male, and most participants had been working in nephrology out-patient clinics for a median of 10.5 years. Overall, CDSS use was poor (81.2%), often due to lack of knowledge about existing CDSS. Most participants (79%) believed CDSS to be helpful in the management of CKD patients with a high willingness to try out a CDSS. Of all adverse event prediction algorithms, prediction of CKD progression (97.8%) and in-silico simulations of disease progression when changing, e. g., lifestyle or medication (97.7%) were rated most important. The spectrum of answers on ethical aspects of CDSS was diverse. CONCLUSION This survey provides insights into experience with and expectations of out-patient nephrologists on CDSS. Despite the current lack of knowledge on CDSS, the willingness to integrate CDSS into daily patient care, and the need for adverse event prediction algorithms was high.
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Affiliation(s)
- Fruzsina Kotsis
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Department of Medicine IV - Nephrology and Primary Care, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Helena Bächle
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Michael Altenbuchinger
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Jürgen Dönitz
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
| | | | - Heike Meiselbach
- Department of Nephrology and Hypertension, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Robin Kosch
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Sabine Salloch
- Institute for Ethics, History and Philosophy of Medicine, Hannover Medical School, Hanover, Germany
| | - Tanja Bratan
- Competence Center Emerging Technologies, Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany
| | - Helena U Zacharias
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hanover, Germany
| | - Ulla T Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
- Department of Medicine IV - Nephrology and Primary Care, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
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