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Yang X, Zhang B, Liu Y, Lv Q, Guo J. Automatic Quantitative Assessment of Muscle Strength Based on Deep Learning and Ultrasound. ULTRASONIC IMAGING 2024; 46:211-219. [PMID: 38881032 DOI: 10.1177/01617346241255590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Skeletal muscle is a vital organ that promotes human movement and maintains posture. Accurate assessment of muscle strength is helpful to provide valuable insights for athletes' rehabilitation and strength training. However, traditional techniques rely heavily on the operator's expertise, which may affect the accuracy of the results. In this study, we propose an automated method to evaluate muscle strength using ultrasound and deep learning techniques. B-mode ultrasound data of biceps brachii of multiple athletes at different strength levels were collected and then used to train our deep learning model. To evaluate the effectiveness of this method, this study tested the contraction of the biceps brachii under different force levels. The classification accuracy of this method for grade 4 and grade 6 muscle strength reached 98% and 96%, respectively, and the overall average accuracy was 93% and 87%, respectively. The experimental results confirm that the innovative methods in this paper can accurately and effectively evaluate and classify muscle strength.
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Affiliation(s)
- Xiao Yang
- Key Laboratory of Ultrasound of Shaanxi Province, School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Beilei Zhang
- Key Laboratory of Ultrasound of Shaanxi Province, School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Ying Liu
- Key Laboratory of Ultrasound of Shaanxi Province, School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Qian Lv
- Key Laboratory of Ultrasound of Shaanxi Province, School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Jianzhong Guo
- Key Laboratory of Ultrasound of Shaanxi Province, School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
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Zhang Z, Du Y, Shi X, Wang K, Qu Q, Liang Q, Ma X, He K, Chi C, Tang J, Liu B, Ji J, Wang J, Dong J, Hu Z, Tian J. NIR-II light in clinical oncology: opportunities and challenges. Nat Rev Clin Oncol 2024; 21:449-467. [PMID: 38693335 DOI: 10.1038/s41571-024-00892-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 05/03/2024]
Abstract
Novel strategies utilizing light in the second near-infrared region (NIR-II; 900-1,880 nm wavelengths) offer the potential to visualize and treat solid tumours with enhanced precision. Over the past few decades, numerous techniques leveraging NIR-II light have been developed with the aim of precisely eliminating tumours while maximally preserving organ function. During cancer surgery, NIR-II optical imaging enables the visualization of clinically occult lesions and surrounding vital structures with increased sensitivity and resolution, thereby enhancing surgical quality and improving patient prognosis. Furthermore, the use of NIR-II light promises to improve cancer phototherapy by enabling the selective delivery of increased therapeutic energy to tissues at greater depths. Initial clinical studies of NIR-II-based imaging and phototherapy have indicated impressive potential to decrease cancer recurrence, reduce complications and prolong survival. Despite the encouraging results achieved, clinical translation of innovative NIR-II techniques remains challenging and inefficient; multidisciplinary cooperation is necessary to bridge the gap between preclinical research and clinical practice, and thus accelerate the translation of technical advances into clinical benefits. In this Review, we summarize the available clinical data on NIR-II-based imaging and phototherapy, demonstrating the feasibility and utility of integrating these technologies into the treatment of cancer. We also introduce emerging NIR-II-based approaches with substantial potential to further enhance patient outcomes, while also highlighting the challenges associated with imminent clinical studies of these modalities.
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Affiliation(s)
- Zeyu Zhang
- Key Laboratory of Big Data-Based Precision Medicine of Ministry of Industry and Information Technology, School of Engineering Medicine, Beihang University, Beijing, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Xiaojing Shi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Qiaojun Qu
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Qian Liang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Ma
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Kunshan He
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Chongwei Chi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Jianqiang Tang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Liu
- Department of General Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiafu Ji
- Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing, China.
| | - Jun Wang
- Thoracic Oncology Institute/Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
| | - Jiahong Dong
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
| | - Zhenhua Hu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China.
| | - Jie Tian
- Key Laboratory of Big Data-Based Precision Medicine of Ministry of Industry and Information Technology, School of Engineering Medicine, Beihang University, Beijing, China.
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China.
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.
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Ma S, Li Y, Yin J, Niu Q, An Z, Du L, Li F, Gu J. Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance. Front Oncol 2024; 14:1374278. [PMID: 38756651 PMCID: PMC11096442 DOI: 10.3389/fonc.2024.1374278] [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: 01/21/2024] [Accepted: 04/19/2024] [Indexed: 05/18/2024] Open
Abstract
Objective In physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers. Methods Female patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves. Results A total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists' group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer. Conclusions The AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.
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Affiliation(s)
- Sai Ma
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanfang Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Yin
- Department of Ultrasound, Shanghai Fourth People’s Hospital, Shanghai, China
| | - Qinghua Niu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zichen An
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lianfang Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fan Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiying Gu
- Department of Ultrasound, Shidong Hospital, Yangpu District, Shanghai, China
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He B, Sun C, Li H, Wang Y, She Y, Zhao M, Fang M, Zhu Y, Wang K, Liu Z, Wei Z, Mu W, Wang S, Tang Z, Wei J, Shao L, Tong L, Huang F, Tang M, Guo Y, Zhang H, Dong D, Chen C, Ma J, Tian J. Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction. Phys Med Biol 2024; 69:075015. [PMID: 38224617 DOI: 10.1088/1361-6560/ad1e7c] [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: 08/31/2023] [Accepted: 01/15/2024] [Indexed: 01/17/2024]
Abstract
Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.
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Affiliation(s)
- Bingxi He
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Caixia Sun
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Yongbo Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, People's Republic of China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Mengmeng Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Mengjie Fang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Yongbei Zhu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Ziqi Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Wei Mu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Shuo Wang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Zhenchao Tang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Lixia Tong
- Neusoft Medical Systems Co. Ltd, Shenyang, People's Republic of China
| | - Feng Huang
- Neusoft Medical Systems Co. Ltd, Shenyang, People's Republic of China
| | - Mingze Tang
- School of Mechanical and Materials Engineering, North China University of Technology, Beijing, People's Republic of China
| | - Yu Guo
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, People's Republic of China
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
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Tagnamas J, Ramadan H, Yahyaouy A, Tairi H. Multi-task approach based on combined CNN-transformer for efficient segmentation and classification of breast tumors in ultrasound images. Vis Comput Ind Biomed Art 2024; 7:2. [PMID: 38273164 PMCID: PMC10811315 DOI: 10.1186/s42492-024-00155-w] [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: 10/16/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
Accurate segmentation of breast ultrasound (BUS) images is crucial for early diagnosis and treatment of breast cancer. Further, the task of segmenting lesions in BUS images continues to pose significant challenges due to the limitations of convolutional neural networks (CNNs) in capturing long-range dependencies and obtaining global context information. Existing methods relying solely on CNNs have struggled to address these issues. Recently, ConvNeXts have emerged as a promising architecture for CNNs, while transformers have demonstrated outstanding performance in diverse computer vision tasks, including the analysis of medical images. In this paper, we propose a novel breast lesion segmentation network CS-Net that combines the strengths of ConvNeXt and Swin Transformer models to enhance the performance of the U-Net architecture. Our network operates on BUS images and adopts an end-to-end approach to perform segmentation. To address the limitations of CNNs, we design a hybrid encoder that incorporates modified ConvNeXt convolutions and Swin Transformer. Furthermore, to enhance capturing the spatial and channel attention in feature maps we incorporate the Coordinate Attention Module. Second, we design an Encoder-Decoder Features Fusion Module that facilitates the fusion of low-level features from the encoder with high-level semantic features from the decoder during the image reconstruction. Experimental results demonstrate the superiority of our network over state-of-the-art image segmentation methods for BUS lesions segmentation.
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Affiliation(s)
- Jaouad Tagnamas
- Department of Informatics, Faculty of Sciences Dhar El Mahraz, University of Sidi Mohamed Ben Abdellah, 30000, Fez, Morocco.
| | - Hiba Ramadan
- Department of Informatics, Faculty of Sciences Dhar El Mahraz, University of Sidi Mohamed Ben Abdellah, 30000, Fez, Morocco
| | - Ali Yahyaouy
- Department of Informatics, Faculty of Sciences Dhar El Mahraz, University of Sidi Mohamed Ben Abdellah, 30000, Fez, Morocco
| | - Hamid Tairi
- Department of Informatics, Faculty of Sciences Dhar El Mahraz, University of Sidi Mohamed Ben Abdellah, 30000, Fez, Morocco
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