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Hattori S, Saggar R, Heidinger E, Qi A, Mullen J, Fee B, Brown CL, Canton SP, Scott D, Hogan MV. Advances in Ultrasound-Guided Surgery and Artificial Intelligence Applications in Musculoskeletal Diseases. Diagnostics (Basel) 2024; 14:2008. [PMID: 39335687 PMCID: PMC11431371 DOI: 10.3390/diagnostics14182008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 08/30/2024] [Accepted: 09/07/2024] [Indexed: 09/30/2024] Open
Abstract
Ultrasound imaging is a vital imaging tool in musculoskeletal medicine, with the number of publications on ultrasound-guided surgery increasing in recent years, especially in minimally invasive procedures of sports, foot and ankle, and hand surgery. However, ultrasound imaging has drawbacks, such as operator dependency and image obscurity. Artificial intelligence (AI) and deep learning (DL), a subset of AI, can address these issues. AI/DL can enhance screening practices for hip dysplasia and osteochondritis dissecans (OCD) of the humeral capitellum, improve diagnostic accuracy for carpal tunnel syndrome (CTS), and provide physicians with better prognostic prediction tools for patients with knee osteoarthritis. Building on these advancements, DL methods, including segmentation, detection, and localization of target tissues and medical instruments, also have the potential to allow physicians and surgeons to perform ultrasound-guided procedures more accurately and efficiently. This review summarizes recent advances in ultrasound-guided procedures for musculoskeletal diseases and provides a comprehensive overview of the utilization of AI/DL in ultrasound for musculoskeletal medicine, particularly focusing on ultrasound-guided surgery.
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Affiliation(s)
- Soichi Hattori
- Foot and Ankle Injury Research (FAIR), Division of Foot and Ankle, Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Rachit Saggar
- Foot and Ankle Injury Research (FAIR), Division of Foot and Ankle, Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Eva Heidinger
- Foot and Ankle Injury Research (FAIR), Division of Foot and Ankle, Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Andrew Qi
- Foot and Ankle Injury Research (FAIR), Division of Foot and Ankle, Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Joseph Mullen
- Foot and Ankle Injury Research (FAIR), Division of Foot and Ankle, Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Brianna Fee
- Foot and Ankle Injury Research (FAIR), Division of Foot and Ankle, Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Cortez L Brown
- Foot and Ankle Injury Research (FAIR), Division of Foot and Ankle, Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Stephen P Canton
- Foot and Ankle Injury Research (FAIR), Division of Foot and Ankle, Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Devon Scott
- Foot and Ankle Injury Research (FAIR), Division of Foot and Ankle, Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - MaCalus V Hogan
- Foot and Ankle Injury Research (FAIR), Division of Foot and Ankle, Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
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Getzmann JM, Zantonelli G, Messina C, Albano D, Serpi F, Gitto S, Sconfienza LM. The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature. LA RADIOLOGIA MEDICA 2024; 129:1405-1411. [PMID: 39001961 PMCID: PMC11379739 DOI: 10.1007/s11547-024-01856-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 07/04/2024] [Indexed: 07/15/2024]
Abstract
PURPOSE To systematically review the use of artificial intelligence (AI) in musculoskeletal (MSK) ultrasound (US) with an emphasis on AI algorithm categories and validation strategies. MATERIAL AND METHODS An electronic literature search was conducted for articles published up to January 2024. Inclusion criteria were the use of AI in MSK US, involvement of humans, English language, and ethics committee approval. RESULTS Out of 269 identified papers, 16 studies published between 2020 and 2023 were included. The research was aimed at predicting diagnosis and/or segmentation in a total of 11 (69%) out of 16 studies. A total of 11 (69%) studies used deep learning (DL)-based algorithms, three (19%) studies employed conventional machine learning (ML)-based algorithms, and two (12%) studies employed both conventional ML- and DL-based algorithms. Six (38%) studies used cross-validation techniques with K-fold cross-validation being the most frequently employed (n = 4, 25%). Clinical validation with separate internal test datasets was reported in nine (56%) papers. No external clinical validation was reported. CONCLUSION AI is a topic of increasing interest in MSK US research. In future studies, attention should be paid to the use of validation strategies, particularly regarding independent clinical validation performed on external datasets.
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Affiliation(s)
| | - Giulia Zantonelli
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy
| | - Carmelo Messina
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy
- UOC Radiodiagnostica, ASST Centro Specialistico Ortopedico Traumatologico Gaetano Pini-CTO, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento Di Scienze Biomediche, Chirurgiche Ed Odontoiatriche, Università Degli Studi Di Milano, Milan, Italy
| | | | - Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy.
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy
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Chen M, Cai R, Zhang A, Chi X, Qian J. The diagnostic value of artificial intelligence-assisted imaging for developmental dysplasia of the hip: a systematic review and meta-analysis. J Orthop Surg Res 2024; 19:522. [PMID: 39210407 PMCID: PMC11360681 DOI: 10.1186/s13018-024-05003-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024] Open
Abstract
OBJECTIVE To clarify the efficacy of artificial intelligence (AI)-assisted imaging in the diagnosis of developmental dysplasia of the hip (DDH) through a meta-analysis. METHODS Relevant literature on AI for early DDH diagnosis was searched in PubMed, Web of Science, Embase, and The Cochrane Library databases until April 4, 2024. The Quality Assessment of Diagnostic Accuracy Studies tool was used to assess the quality of included studies. Revman5.4 and StataSE-64 software were used to calculate the combined sensitivity, specificity, AUC value, and DOC value of AI-assisted imaging for DDH diagnosis. RESULTS The meta-analysis included 13 studies (6 prospective and 7 retrospective) with 28 AI models and a total of 10,673 samples. The summary sensitivity, specificity, AUC value, and DOC value were 99.0% (95% CI: 97.0-100.0%), 94.0% (95% CI: 89.0-96.0%), 99.0% (95% CI: 98.0-100.0%), and 1342 (95% CI: 469-3842), respectively. CONCLUSION AI-assisted imaging demonstrates high diagnostic efficacy for DDH detection, improving the accuracy of early DDH imaging examination. More prospective studies are needed to further confirm the value of AI-assisted imaging for early DDH diagnosis.
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Affiliation(s)
- Min Chen
- Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China
| | - Ruyi Cai
- Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China
| | - Aixia Zhang
- Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China
| | - Xia Chi
- Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China
- School of Pediatrics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China
| | - Jun Qian
- Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China.
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Huang T, Shi J, Li J, Wang J, Du J, Shi J. Involution Transformer Based U-Net for Landmark Detection in Ultrasound Images for Diagnosis of Infantile DDH. IEEE J Biomed Health Inform 2024; 28:4797-4809. [PMID: 38630567 DOI: 10.1109/jbhi.2024.3390241] [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: 04/19/2024]
Abstract
The B-mode ultrasound based computer-aided diagnosis (CAD) has demonstrated its effectiveness for diagnosis of Developmental Dysplasia of the Hip (DDH) in infants, which can conduct the Graf's method by detecting landmarks in hip ultrasound images. However, it is still necessary to explore more valuable information around these landmarks to enhance feature representation for improving detection performance in the detection model. To this end, a novel Involution Transformer based U-Net (IT-UNet) network is proposed for hip landmark detection. The IT-UNet integrates the efficient involution operation into Transformer to develop an Involution Transformer module (ITM), which consists of an involution attention block and a squeeze-and-excitation involution block. The ITM can capture both the spatial-related information and long-range dependencies from hip ultrasound images to effectively improve feature representation. Moreover, an Involution Downsampling block (IDB) is developed to alleviate the issue of feature loss in the encoder modules, which combines involution and convolution for the purpose of downsampling. The experimental results on two DDH ultrasound datasets indicate that the proposed IT-UNet achieves the best landmark detection performance, indicating its potential applications.
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Chen YP, Fan TY, Chu CC, Lin JJ, Ji CY, Kuo CF, Kao HK. Automatic and human level Graf's type identification for detecting developmental dysplasia of the hip. Biomed J 2024; 47:100614. [PMID: 37308078 PMCID: PMC10955653 DOI: 10.1016/j.bj.2023.100614] [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: 02/14/2023] [Revised: 05/11/2023] [Accepted: 06/07/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Developmental dysplasia of the hip (DDH) is a common congenital disorder that may lead to hip dislocation and requires surgical intervention if left untreated. Ultrasonography is the preferred method for DDH screening; however, the lack of experienced operators impedes its application in universal neonatal screening. METHODS We developed a deep neural network tool to automatically register the five keypoints that mark important anatomical structures of the hip and provide a reference for measuring alpha and beta angles following Graf's guidelines, which is an ultrasound classification system for DDH in infants. Two-dimensional (2D) ultrasonography images were obtained from 986 neonates aged 0-6 months. A total of 2406 images from 921 patients were labeled with ground truth keypoints by senior orthopedists. RESULTS Our model demonstrated precise keypoint localization. The mean absolute error was approximately 1 mm, and the derived alpha angle measurement had a correlation coefficient of R = 0.89 between the model and ground truth. The model achieved an area under the receiver operating characteristic curve of 0.937 and 0.974 for classifying alpha <60° (abnormal hip) and <50° (dysplastic hip), respectively. On average, the experts agreed with 96% of the inferenced images, and the model could generalize its prediction on newly collected images with a correlation coefficient higher than 0.85. CONCLUSIONS Precise localization and highly correlated performance metrics suggest that the model can be an efficient tool for assisting DDH diagnosis in clinical settings.
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Affiliation(s)
- Yueh-Peng Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Master of Science Degree Program in Innovation for Smart Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Tzuo-Yau Fan
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Department of Artificial Intelligence Research and Development, Chang Gung Medical Technology Co., Ltd., Linkou, Taiwan
| | - Cheng-Cj Chu
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Jainn-Jim Lin
- Division of Pediatric Critical Care Medicine and Pediatric Neurocritical Care Center, Chang Gung Children's Hospital and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chin-Yi Ji
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Department of Artificial Intelligence Research and Development, Chang Gung Medical Technology Co., Ltd., Linkou, Taiwan
| | - Chang-Fu Kuo
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
| | - Hsuan-Kai Kao
- Department of Orthopedic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Bone and Joint Research Center, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan.
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Duan X, Yang L, Zhu W, Yuan H, Xu X, Wen H, Liu W, Chen M. Is the diagnostic model based on convolutional neural network superior to pediatric radiologists in the ultrasonic diagnosis of biliary atresia? Front Med (Lausanne) 2024; 10:1308338. [PMID: 38259860 PMCID: PMC10800889 DOI: 10.3389/fmed.2023.1308338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
Background Many screening and diagnostic methods are currently available for biliary atresia (BA), but the early and accurate diagnosis of BA remains a challenge with existing methods. This study aimed to use deep learning algorithms to intelligently analyze the ultrasound image data, build a BA ultrasound intelligent diagnostic model based on the convolutional neural network, and realize an intelligent diagnosis of BA. Methods A total of 4,887 gallbladder ultrasound images of infants with BA, non-BA hyperbilirubinemia, and healthy infants were collected. Two mask region convolutional neural network (Mask R-CNN) models based on different backbone feature extraction networks were constructed. The diagnostic performance between the two models was compared through good-quality images at the image level and the patient level. The diagnostic performance between the two models was compared through poor-quality images. The diagnostic performance of BA between the model and four pediatric radiologists was compared at the image level and the patient level. Results The classification performance of BA in model 2 was slightly higher than that in model 1 in the test set, both at the image level and at the patient level, with a significant difference of p = 0.0365 and p = 0.0459, respectively. The classification accuracy of model 2 was slightly higher than that of model 1 in poor-quality images (88.3% vs. 86.4%), and the difference was not statistically significant (p = 0.560). The diagnostic performance of model 2 was similar to that of the two radiology experts at the image level, and the differences were not statistically significant. The diagnostic performance of model 2 in the test set was higher than that of the two radiology experts at the patient level (all p < 0.05). Conclusion The performance of model 2 based on Mask R-CNN in the diagnosis of BA reached or even exceeded the level of pediatric radiology experts.
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Affiliation(s)
- Xingxing Duan
- Department of Ultrasound, Changsha Hospital for Maternal and Child Health Care, Changsha, China
| | - Liu Yang
- Department of Ultrasound, Hunan Children’s Hospital, Changsha, China
| | - Weihong Zhu
- Department of Ultrasound, Chenzhou Children’s Hospital, Chenzhou, China
| | - Hongxia Yuan
- Department of Ultrasound, Changsha Hospital for Maternal and Child Health Care, Changsha, China
| | - Xiangfen Xu
- Department of Ultrasound, Hunan Children’s Hospital, Changsha, China
| | - Huan Wen
- Department of Ultrasound, Hunan Children’s Hospital, Changsha, China
| | - Wengang Liu
- Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Meiyan Chen
- Department of Ultrasound, Chaling Hospital for Maternal and Child Health Care, Chaling, China
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Jan F, Rahman A, Busaleh R, Alwarthan H, Aljaser S, Al-Towailib S, Alshammari S, Alhindi KR, Almogbil A, Bubshait DA, Ahmed MIB. Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach. J Imaging 2023; 9:242. [PMID: 37998088 PMCID: PMC10672484 DOI: 10.3390/jimaging9110242] [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: 09/30/2023] [Revised: 10/25/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023] Open
Abstract
Developmental dysplasia of the hip (DDH) is a disorder characterized by abnormal hip development that frequently manifests in infancy and early childhood. Preventing DDH from occurring relies on a timely and accurate diagnosis, which requires careful assessment by medical specialists during early X-ray scans. However, this process can be challenging for medical personnel to achieve without proper training. To address this challenge, we propose a computational framework to detect DDH in pelvic X-ray imaging of infants that utilizes a pipelined deep learning-based technique consisting of two stages: instance segmentation and keypoint detection models to measure acetabular index angle and assess DDH affliction in the presented case. The main aim of this process is to provide an objective and unified approach to DDH diagnosis. The model achieved an average pixel error of 2.862 ± 2.392 and an error range of 2.402 ± 1.963° for the acetabular angle measurement relative to the ground truth annotation. Ultimately, the deep-learning model will be integrated into the fully developed mobile application to make it easily accessible for medical specialists to test and evaluate. This will reduce the burden on medical specialists while providing an accurate and explainable DDH diagnosis for infants, thereby increasing their chances of successful treatment and recovery.
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Affiliation(s)
- Farmanullah Jan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Atta Rahman
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Roaa Busaleh
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Haya Alwarthan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Samar Aljaser
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Sukainah Al-Towailib
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Safiyah Alshammari
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Khadeejah Rasheed Alhindi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Asrar Almogbil
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Dalal A. Bubshait
- Department of Orthopedics, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Mohammed Imran Basheer Ahmed
- Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
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Ding AS, Lu A, Li Z, Sahu M, Galaiya D, Siewerdsen JH, Unberath M, Taylor RH, Creighton FX. A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging. Otolaryngol Head Neck Surg 2023; 169:988-998. [PMID: 36883992 PMCID: PMC11060418 DOI: 10.1002/ohn.317] [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: 10/18/2022] [Revised: 01/19/2023] [Accepted: 02/19/2023] [Indexed: 03/09/2023]
Abstract
OBJECTIVE Preoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time-consuming. Automated methods for segmenting multiple geometrically complex structures can not only streamline preoperative planning but also augment minimally invasive and/or robot-assisted procedures in this space. This study evaluates a state-of-the-art deep learning pipeline for semantic segmentation of temporal bone anatomy. STUDY DESIGN A descriptive study of a segmentation network. SETTING Academic institution. METHODS A total of 15 high-resolution cone-beam temporal bone computed tomography (CT) data sets were included in this study. All images were co-registered, with relevant anatomical structures (eg, ossicles, inner ear, facial nerve, chorda tympani, bony labyrinth) manually segmented. Predicted segmentations from no new U-Net (nnU-Net), an open-source 3-dimensional semantic segmentation neural network, were compared against ground-truth segmentations using modified Hausdorff distances (mHD) and Dice scores. RESULTS Fivefold cross-validation with nnU-Net between predicted and ground-truth labels were as follows: malleus (mHD: 0.044 ± 0.024 mm, dice: 0.914 ± 0.035), incus (mHD: 0.051 ± 0.027 mm, dice: 0.916 ± 0.034), stapes (mHD: 0.147 ± 0.113 mm, dice: 0.560 ± 0.106), bony labyrinth (mHD: 0.038 ± 0.031 mm, dice: 0.952 ± 0.017), and facial nerve (mHD: 0.139 ± 0.072 mm, dice: 0.862 ± 0.039). Comparison against atlas-based segmentation propagation showed significantly higher Dice scores for all structures (p < .05). CONCLUSION Using an open-source deep learning pipeline, we demonstrate consistently submillimeter accuracy for semantic CT segmentation of temporal bone anatomy compared to hand-segmented labels. This pipeline has the potential to greatly improve preoperative planning workflows for a variety of otologic and neurotologic procedures and augment existing image guidance and robot-assisted systems for the temporal bone.
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Affiliation(s)
- Andy S. Ding
- Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Alexander Lu
- Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Zhaoshuo Li
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Manish Sahu
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Deepa Galaiya
- Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jeffrey H. Siewerdsen
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Russell H. Taylor
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Francis X. Creighton
- Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Sezer A, Sezer HB. Segmentation of measurable images from standard plane of Graf hip ultrasonograms based on Mask Region-Based Convolutional Neural Network. Jt Dis Relat Surg 2023; 34:590-597. [PMID: 37750263 PMCID: PMC10546856 DOI: 10.52312/jdrs.2023.1308] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 07/29/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVES This study proposed a Mask Region-Based Convolutional Neural Network (R-CNN)-based automatic segmentation to accurately detect the measurable standard plane of Graf hip ultrasonography images via segmentation of the labrum, lower limb of ilium, and the iliac wing. PATIENTS AND METHODS The study examined the hip ultrasonograms of 675 infants (205 males, 470 females; mean age: 7±2.8 weeks; range, 3 to 20 weeks) recorded between January 2011 and January 2018. The standard plane newborn hip ultrasound images were classified according to Graf's method by an experienced ultrasonographer. The hips were grouped as type 1, type 2a, type 2b, and type 2c-D. Two hundred seventy-five ultrasonograms were utilized as training data, 30 were validation data, and 370 were test data. The three anatomical regions were simultaneously segmented by Mask-R CNN in the test data and defective ultrasonograms. Automatic instance-based segmentation results were compared with the manual segmentation results of an experienced orthopedic expert. Success rates were calculated using Dice and mean average precision (mAP) metrics. RESULTS Of these, 447 Graf type 1, 175 type 2a or 2b, 53 type 2c and D ultrasonograms were utilized. Average success rates with respect to hip types in the whole data were 96.95 and 96.96% according to Dice and mAP methods, respectively. Average success rates with respect to anatomical regions were 97.20 and 97.35% according to Dice and mAP methods, respectively. The highest average success rates were for type 1 hips, with 98.46 and 98.73%, and the iliac wing, with 98.25 and 98.86%, according to Dice and mAP methods, respectively. CONCLUSION Mask R-CNN is a robust instance-based method in the segmentation of Graf hip ultrasonograms to delineate the standard plane. The proposed method revealed high success in each type of hip for each anatomic region.
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Affiliation(s)
- Aysun Sezer
- Biruni Üniversitesi, Bilgisayar Mühendisliği, 34010 Zeytinburnu, İstanbul, Türkiye.
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Huang B, Xia B, Qian J, Zhou X, Zhou X, Liu S, Chang A, Yan Z, Tang Z, Xu N, Tao H, He X, Yu W, Zhang R, Huang R, Ni D, Yang X. Artificial Intelligence-Assisted Ultrasound Diagnosis on Infant Developmental Dysplasia of the Hip Under Constrained Computational Resources. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:1235-1248. [PMID: 36445006 DOI: 10.1002/jum.16133] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/31/2022] [Accepted: 11/06/2022] [Indexed: 05/18/2023]
Abstract
OBJECTIVES Ultrasound (US) is important for diagnosing infant developmental dysplasia of the hip (DDH). However, the accuracy of the diagnosis depends heavily on expertise. We aimed to develop a novel automatic system (DDHnet) for accurate, fast, and robust diagnosis of DDH. METHODS An automatic system, DDHnet, was proposed to diagnose DDH by analyzing static ultrasound images. A five-fold cross-validation experiment was conducted using a dataset containing 881 patients to verify the performance of DDHnet. In addition, a blind test was conducted on 209 patients (158 normal and 51 abnormal cases). The feasibility and performance of DDHnet were investigated by embedding it into ultrasound machines at low computational cost. RESULTS DDHnet obtained reliable measurements and accurate diagnosis predictions. It reported an intra-class correlation coefficient (ICC) on α angle of 0.96 (95% CI: 0.93-0.97), β angle of 0.97 (95% CI: 0.95-0.98), FHC of 0.98 (95% CI: 0.96-0.99) and PFD of 0.94 (95% CI: 0.90-0.96) in abnormal cases. DDHnet achieved a sensitivity of 90.56%, specificity of 100%, accuracy of 98.64%, positive predictive value (PPV) of 100%, and negative predictive value (NPV) of 98.44% for the diagnosis of DDH. For the measurement task on the US device, DDHnet took only 1.1 seconds to operate and complete, whereas the experienced senior expert required an average 41.4 seconds. CONCLUSIONS The proposed DDHnet demonstrate state-of-the-art performance for all four indicators of DDH diagnosis. Fast and highly accurate DDH diagnosis is achievable through DDHnet, and is accessible under constrained computational resources.
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Affiliation(s)
- Bingxuan Huang
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Bei Xia
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Jikuan Qian
- R&D Department, Shenzhen RayShape Medical Technology Co. Ltd., Shenzhen, China
| | - Xinrui Zhou
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xu Zhou
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Shengfeng Liu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Ao Chang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Zhongnuo Yan
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Zijian Tang
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Na Xu
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Hongwei Tao
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Xuezhi He
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Wei Yu
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Renfu Zhang
- Ultrasound Department, EDAN Instruments, Inc., Shenzhen, China
| | - Ruobing Huang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xin Yang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
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The Diagnosis of Developmental Dysplasia of the Hip From Hip Ultrasonography Images With Deep Learning Methods. J Pediatr Orthop 2023; 43:e132-e137. [PMID: 36344482 DOI: 10.1097/bpo.0000000000002294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Hip ultrasonography is very important in the early diagnosis of developmental dysplasia of the hip. The application of deep learning-based medical image analysis to computer-aided diagnosis has the potential to provide decision-making support to clinicians and improve the accuracy and efficiency of various diagnostic and treatment processes. This has encouraged new research and development efforts in computer-aided diagnosis. The aim of this study was to evaluate hip sonograms using computer-assisted deep-learning methods. METHODS The study included 376 sonograms evaluated as normal according to the Graf method, 541 images with dysplasia and 365 images with incorrect probe position. To classify the developmental hip dysplasia ultrasound images, transfer learning was applied with pretrained VGG-16, ResNet-101, MobileNetV2 and GoogLeNet networks. The performances of the networks were evaluated with the performance parameters of accuracy, sensitivity, specificity, precision, F1 score, and AUC (area under the ROC curve). RESULTS The accuracy, sensitivity, specificity, precision, F1 score, and AUC results obtained by testing the VGG-16, ResNet-101, MobileNetV2, and GoogLeNet models showed performance >80%. With the pretrained VGG-19 model, 93%, 93.5%, 96.7%, 92.3%, 92.6%, and 0.99 accuracy, sensitivity, specificity, precision, F1 score, and AUC results were obtained, respectively. CONCLUSION In this study, in addition to the ultrasonography images of dysplastic and healthy hips, images were also included of probe malpositioning, and these images were able to be successfully evaluated with deep learning methods. On the sonograms, which provided criteria appropriate for evaluation, successful differentiation could be made of healthy hips and dysplastic hips. LEVEL OF EVIDENCE Level-IV; diagnostic studies.
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12
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Lee SW, Ye HU, Lee KJ, Jang WY, Lee JH, Hwang SM, Heo YR. Reply to Çiftci, S.; Aydin, B.K. Comment on "Lee et al. Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening. Diagnostics 2021, 11, 1174". Diagnostics (Basel) 2022; 12:1739. [PMID: 35885643 PMCID: PMC9320449 DOI: 10.3390/diagnostics12071739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 12/03/2022] Open
Abstract
We thank Dr. Sadettin Ciftci for his comment on the key point issues in measuring the alpha and beta angle with Graf method. We appreciated his feedback [...].
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Affiliation(s)
- Si-Wook Lee
- Department of Orthopedic Surgery, Dongsan Medical Center, School of Medicine, Keimyung University, Daegu 42601, Korea; (H.-U.Y.); (K.-J.L.)
| | - Hee-Uk Ye
- Department of Orthopedic Surgery, Dongsan Medical Center, School of Medicine, Keimyung University, Daegu 42601, Korea; (H.-U.Y.); (K.-J.L.)
| | - Kyung-Jae Lee
- Department of Orthopedic Surgery, Dongsan Medical Center, School of Medicine, Keimyung University, Daegu 42601, Korea; (H.-U.Y.); (K.-J.L.)
| | - Woo-Young Jang
- Department of Orthopedic Surgery, Korea University Anam Hospital, 73, Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea;
| | - Jong-Ha Lee
- Department of Biomedical Engineering, Keimyung University, Daegu 42601, Korea; (J.-H.L.); (S.-M.H.)
| | - Seok-Min Hwang
- Department of Biomedical Engineering, Keimyung University, Daegu 42601, Korea; (J.-H.L.); (S.-M.H.)
| | - Yu-Ran Heo
- Department of Anatomy, Dongsan Medical Center, School of Medicine, Keimyung University, Daegu 42601, Korea;
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13
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Comment on Lee et al. Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening. Diagnostics 2021, 11, 1174. Diagnostics (Basel) 2022; 12:diagnostics12071738. [PMID: 35885642 PMCID: PMC9320642 DOI: 10.3390/diagnostics12071738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/01/2022] [Accepted: 06/21/2022] [Indexed: 11/16/2022] Open
Abstract
We have read the article titled “Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening” by Lee et al. [...]
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Chen T, Zhang Y, Wang B, Wang J, Cui L, He J, Cong L. Development of a Fully Automated Graf Standard Plane and Angle Evaluation Method for Infant Hip Ultrasound Scans. Diagnostics (Basel) 2022; 12:diagnostics12061423. [PMID: 35741233 PMCID: PMC9222165 DOI: 10.3390/diagnostics12061423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/04/2022] [Accepted: 06/07/2022] [Indexed: 11/18/2022] Open
Abstract
Background: Graf’s method is currently the most commonly used ultrasound-based technique for the diagnosis of developmental dysplasia of the hip (DDH). However, the efficiency and accuracy of diagnosis are highly affected by the sonographers’ qualification and the time and effort expended, which has a significant intra- and inter-observer variability. Methods: Aiming to minimize the manual intervention in the diagnosis process, we developed a deep learning-based computer-aided framework for the DDH diagnosis, which can perform fully automated standard plane detection and angle measurement for Graf type I and type II hips. The proposed framework is composed of three modules: an anatomical structure detection module, a standard plane scoring module, and an angle measurement module. This framework can be applied to two common clinical scenarios. The first is the static mode, measurement and classification are performed directly based on the given standard plane. The second is the dynamic mode, where a standard plane from ultrasound video is first determined, and measurement and classification are then completed. To the best of our knowledge, our proposed framework is the first CAD method that can automatically perform the entire measurement process of Graf’s method. Results: In our experiments, 1051 US images and 289 US videos of Graf type I and type II hips were used to evaluate the performance of the proposed framework. In static mode, the mean absolute error of α, β angles are 1.71° and 2.40°, and the classification accuracy is 94.71%. In dynamic mode, the mean absolute error of α, β angles are 1.97° and 2.53°, the classification accuracy is 89.51%, and the running speed is 31 fps. Conclusions: Experimental results demonstrate that our fully automated framework can accurately perform standard plane detection and angle measurement of an infant’s hip at a fast speed, showing great potential for clinical application.
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Affiliation(s)
- Tao Chen
- Department of Ultrasound, Beijing Jishuitan Hospital, The 4th Clinical College, Peking University, Beijing 100035, China;
- Correspondence: (T.C.); (L.C.)
| | - Yuxiao Zhang
- Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen 518057, China; (Y.Z.); (B.W.); (J.W.)
| | - Bo Wang
- Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen 518057, China; (Y.Z.); (B.W.); (J.W.)
| | - Jian Wang
- Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen 518057, China; (Y.Z.); (B.W.); (J.W.)
| | - Ligang Cui
- Department of Ultrasound, Peking University Third Hospital, Beijing 100191, China;
| | - Jingnan He
- Department of Ultrasound, Beijing Jishuitan Hospital, The 4th Clinical College, Peking University, Beijing 100035, China;
| | - Longfei Cong
- Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen 518057, China; (Y.Z.); (B.W.); (J.W.)
- Correspondence: (T.C.); (L.C.)
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