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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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Cheng C, Liang X, Guo D, Xie D. Application of Artificial Intelligence in Shoulder Pathology. Diagnostics (Basel) 2024; 14:1091. [PMID: 38893618 PMCID: PMC11171621 DOI: 10.3390/diagnostics14111091] [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: 04/02/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.
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Affiliation(s)
- Cong Cheng
- Department of Orthopaedics, People’s Hospital of Longhua, Shenzhen 518000, China;
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Xinzhi Liang
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Dong Guo
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Denghui Xie
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
- Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
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Guo WL, Geng AK, Geng C, Wang J, Dai YK. Combination of UNet++ and ResNeSt to classify chronic inflammation of the choledochal cystic wall in patients with pancreaticobiliary maljunction. Br J Radiol 2022; 95:20201189. [PMID: 35451311 PMCID: PMC10996311 DOI: 10.1259/bjr.20201189] [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: 10/02/2020] [Revised: 03/10/2022] [Accepted: 04/01/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES The aim of this study was to establish an automatic classification model for chronic inflammation of the choledoch wall using deep learning with CT images in patients with pancreaticobiliary maljunction (PBM). METHODS CT images were obtained from 76 PBM patients, including 61 cases assigned to the training set and 15 cases assigned to the testing set. The region of interest (ROI) containing the choledochal lesion was extracted and segmented using the UNet++ network. The degree of severity of inflammation in the choledochal wall was initially classified using the ResNeSt network. The final classification result was determined per decision rules. Grad-CAM was used to explain the association between the classification basis of the network and clinical diagnosis. RESULTS Segmentation of the lesion on the common bile duct wall was roughly obtained with the UNet++ segmentation model and the average value of Dice coefficient of the segmentation model in the testing set was 0.839 ± 0.150, which was verified through fivefold cross-validation. Inflammation was initially classified with ResNeSt18, which resulted in accuracy = 0.756, sensitivity = 0.611, specificity = 0.852, precision = 0.733, and area under curve (AUC) = 0.711. The final classification sensitivity was 0.8. Grad-CAM revealed similar distribution of inflammation of the choledochal wall and verified the inflammation classification. CONCLUSIONS By combining the UNet++ network and the ResNeSt network, we achieved automatic classification of chronic inflammation of the choledoch in PBM patients and verified the robustness through cross-validation performed five times. This study provided an important basis for classification of inflammation severity of the choledoch in PBM patients. ADVANCES IN KNOWLEDGE We combined the UNet++ network and the ResNeSt network to achieve automatic classification of chronic inflammation of the choledoch in PBM. These results provided an important basis for classification of choledochal inflammation in PBM and for surgical therapy.
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Affiliation(s)
- Wan-liang Guo
- Department of Radiology, Children’s Hospital of Soochow
University, Suzhou,
China
| | - An-kang Geng
- School of Biomedical Engineering (Suzhou), Division of Life
Sciences and Medicine, University of Science and Technology of China, 88
Keling Road, Suzhou,
China
- Suzhou Institute of Biomedical Engineering and Technology,
Chinese Academy of Sciences, 88 Keling Road,
Suzhou, China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology,
Chinese Academy of Sciences, 88 Keling Road,
Suzhou, China
| | - Jian Wang
- Pediatric Surgery, Children’s Hospital of Soochow
University, Suzhou,
China
| | - Ya-kang Dai
- Suzhou Institute of Biomedical Engineering and Technology,
Chinese Academy of Sciences, 88 Keling Road,
Suzhou, China
- Jinan Guoke Medical Engineering Technology Development Co.
LTD, Jinan,
China
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Wang B, Perronne L, Burke C, Adler RS. Artificial Intelligence for Classification of Soft-Tissue Masses at US. Radiol Artif Intell 2020; 3:e200125. [PMID: 33937855 DOI: 10.1148/ryai.2020200125] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 10/05/2020] [Accepted: 10/28/2020] [Indexed: 12/17/2022]
Abstract
Purpose To train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commonly observed benign masses. Materials and Methods In this retrospective study, US images obtained between May 2010 and June 2019 from 419 patients (mean age, 52 years ± 18 [standard deviation]; 250 women) with histologic diagnosis confirmed at biopsy or surgical excision (n = 227) or masses that demonstrated imaging characteristics of lipoma, benign peripheral nerve sheath tumor, and vascular malformation (n = 192) were included. Images in patients with a histologic diagnosis (n = 227) were used to train and evaluate a CNN model to distinguish malignant and benign lesions. Twenty percent of cases were withheld as a test dataset, and the remaining cases were used to train the model with a 75%-25% training-validation split and fourfold cross-validation. Performance of the model was compared with retrospective interpretation of the same dataset by two experienced musculoskeletal radiologists, blinded to clinical history. A second group of US images from 275 of the 419 patients containing the three common benign masses was used to train and evaluate a separate model to differentiate between the masses. The models were trained on the Keras machine learning platform (version 2.3.1), with a modified pretrained VGG16 network. Performance metrics of the model and of the radiologists were compared by using the McNemar test, and 95% CIs for performance metrics were estimated by using the Clopper-Pearson method (accuracy, recall, specificity, and precision) and the DeLong method (area under the receiver operating characteristic curve). Results The model trained to classify malignant and benign masses demonstrated an accuracy of 79% (95% CI: 68, 88) on the test data, with an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.84, 0.98), matching the performance of two expert readers. Performance of the model distinguishing three benign masses was lower, with an accuracy of 71% (95% CI: 61, 80) on the test data. Conclusion The trained CNN was capable of differentiating between benign and malignant soft-tissue masses depicted on US images, with performance matching that of two experienced musculoskeletal radiologists.© RSNA, 2020.
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Affiliation(s)
- Benjamin Wang
- Department of Radiology, Division of Musculoskeletal Radiology, NYU Langone Health, 301 E 17th St, 6th Floor, New York, NY, 10003 (B.W., C.B., R.S.A.); and Department of Musculoskeletal Imaging, Hôpital Lariboisière, Paris, France (L.P.)
| | - Laetitia Perronne
- Department of Radiology, Division of Musculoskeletal Radiology, NYU Langone Health, 301 E 17th St, 6th Floor, New York, NY, 10003 (B.W., C.B., R.S.A.); and Department of Musculoskeletal Imaging, Hôpital Lariboisière, Paris, France (L.P.)
| | - Christopher Burke
- Department of Radiology, Division of Musculoskeletal Radiology, NYU Langone Health, 301 E 17th St, 6th Floor, New York, NY, 10003 (B.W., C.B., R.S.A.); and Department of Musculoskeletal Imaging, Hôpital Lariboisière, Paris, France (L.P.)
| | - Ronald S Adler
- Department of Radiology, Division of Musculoskeletal Radiology, NYU Langone Health, 301 E 17th St, 6th Floor, New York, NY, 10003 (B.W., C.B., R.S.A.); and Department of Musculoskeletal Imaging, Hôpital Lariboisière, Paris, France (L.P.)
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Yi J, Kang HK, Kwon JH, Kim KS, Park MH, Seong YK, Kim DW, Ahn B, Ha K, Lee J, Hah Z, Bang WC. Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency. Ultrasonography 2020; 40:7-22. [PMID: 33152846 PMCID: PMC7758107 DOI: 10.14366/usg.20102] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 09/14/2020] [Indexed: 12/12/2022] Open
Abstract
In this review of the most recent applications of deep learning to ultrasound imaging, the architectures of deep learning networks are briefly explained for the medical imaging applications of classification, detection, segmentation, and generation. Ultrasonography applications for image processing and diagnosis are then reviewed and summarized, along with some representative imaging studies of the breast, thyroid, heart, kidney, liver, and fetal head. Efforts towards workflow enhancement are also reviewed, with an emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented regarding image quality enhancement, diagnostic support, and improvements in workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.
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Affiliation(s)
- Jonghyon Yi
- Ultrasound R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam, Korea
| | - Ho Kyung Kang
- Ultrasound R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam, Korea
| | - Jae-Hyun Kwon
- DR Imaging R&D Lab, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam, Korea
| | - Kang-Sik Kim
- Ultrasound R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam, Korea
| | - Moon Ho Park
- Ultrasound R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam, Korea
| | - Yeong Kyeong Seong
- Ultrasound R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam, Korea
| | - Dong Woo Kim
- Product Strategy Group, Samsung Medison Co., Ltd., Seongnam, Korea
| | - Byungeun Ahn
- Product Strategy Group, Samsung Medison Co., Ltd., Seongnam, Korea
| | - Kilsu Ha
- Product Strategy Group, Samsung Medison Co., Ltd., Seongnam, Korea
| | - Jinyong Lee
- System R&D Group, Samsung Medison Co., Ltd., Seongnam, Korea
| | - Zaegyoo Hah
- System R&D Group, Samsung Medison Co., Ltd., Seongnam, Korea
| | - Won-Chul Bang
- Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Korea.,Product Strategy Team, Samsung Medison Co., Ltd., Seoul, Korea
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