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Liu D, Yang K, Zhang C, Xiao D, Zhao Y. Fully-Automatic Detection and Diagnosis System for Thyroid Nodules Based on Ultrasound Video Sequences by Artificial Intelligence. J Multidiscip Healthc 2024; 17:1641-1651. [PMID: 38646015 PMCID: PMC11027922 DOI: 10.2147/jmdh.s439629] [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] [Received: 09/10/2023] [Accepted: 04/08/2024] [Indexed: 04/23/2024] Open
Abstract
Background Interpretation of ultrasound findings of thyroid nodules is subjective and labor-intensive for radiologists. Artificial intelligence (AI) is a relatively objective and efficient technology. We aimed to establish a fully automatic detection and diagnosis system for thyroid nodules based on AI technology by analyzing ultrasound video sequences. Patients and Methods We prospectively acquired dynamic ultrasound videos of 1067 thyroid nodules (804 for training and 263 for validation) from December 2018 to January 2021. All the patients underwent hemithyroidectomy or total thyroidectomy. Dynamic ultrasound videos were used to develop an AI system consisting of two deep learning models that could automatically detect and diagnose thyroid nodules. Average precision (AP) was used to estimate the performance of the detection model. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of the diagnostic model. Results Location and shape were accurately detected with a high AP of 0.914 in the validation cohort. The AUC of the diagnostic model was 0.953 in the validation cohort. The sensitivity and specificity of junior and senior radiologists were 76.9% vs 78.3% and 68.4% vs 81.1%, respectively. The diagnostic performance of the AI diagnostic model was superior to that of junior radiologists (P = 0.016) and was not significantly different from that of senior radiologists (P = 0.281). Conclusion We established a fully automatic detection and diagnosis system for thyroid nodules based on ultrasound video using an AI approach that can be conveniently applied to optimize the management of patients with thyroid nodules.
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Affiliation(s)
- Dan Liu
- Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China
| | - Ke Yang
- The First in-Patient Department, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, People’s Republic of China
| | - Chunquan Zhang
- Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China
| | - Dandan Xiao
- Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China
| | - Yu Zhao
- Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China
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Yin Y, Tang Z, Weng H. Application of visual transformer in renal image analysis. Biomed Eng Online 2024; 23:27. [PMID: 38439100 PMCID: PMC10913284 DOI: 10.1186/s12938-024-01209-z] [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: 09/20/2023] [Accepted: 01/22/2024] [Indexed: 03/06/2024] Open
Abstract
Deep Self-Attention Network (Transformer) is an encoder-decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis.
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Affiliation(s)
- Yuwei Yin
- The College of Health Sciences and Engineering, University of Shanghai for Science and Technology, 516 Jungong Highway, Yangpu Area, Shanghai, 200093, China
- The College of Medical Technology, Shanghai University of Medicine & Health Sciences, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China
| | - Zhixian Tang
- The College of Medical Technology, Shanghai University of Medicine & Health Sciences, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China.
| | - Huachun Weng
- The College of Health Sciences and Engineering, University of Shanghai for Science and Technology, 516 Jungong Highway, Yangpu Area, Shanghai, 200093, China.
- The College of Medical Technology, Shanghai University of Medicine & Health Sciences, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China.
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Xu T, Zhang XY, Yang N, Jiang F, Chen GQ, Pan XF, Peng YX, Cui XW. A narrative review on the application of artificial intelligence in renal ultrasound. Front Oncol 2024; 13:1252630. [PMID: 38495082 PMCID: PMC10943690 DOI: 10.3389/fonc.2023.1252630] [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: 07/14/2023] [Accepted: 12/12/2023] [Indexed: 03/19/2024] Open
Abstract
Kidney disease is a serious public health problem and various kidney diseases could progress to end-stage renal disease. The many complications of end-stage renal disease. have a significant impact on the physical and mental health of patients. Ultrasound can be the test of choice for evaluating the kidney and perirenal tissue as it is real-time, available and non-radioactive. To overcome substantial interobserver variability in renal ultrasound interpretation, artificial intelligence (AI) has the potential to be a new method to help radiologists make clinical decisions. This review introduces the applications of AI in renal ultrasound, including automatic segmentation of the kidney, measurement of the renal volume, prediction of the kidney function, diagnosis of the kidney diseases. The advantages and disadvantages of the applications will also be presented clinicians to conduct research. Additionally, the challenges and future perspectives of AI are discussed.
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Affiliation(s)
- Tong Xu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xian-Ya Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Na Yang
- Department of Ultrasound, Affiliated Hospital of Jilin Medical College, Jilin, China
| | - Fan Jiang
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian, China
| | - Yue-Xiang Peng
- Department of Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Oghli MG, Bagheri SM, Shabanzadeh A, Mehrjardi MZ, Akhavan A, Shiri I, Taghipour M, Shabanzadeh Z. Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet+. Sci Rep 2024; 14:4782. [PMID: 38413748 PMCID: PMC10899245 DOI: 10.1038/s41598-024-55106-5] [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: 06/25/2023] [Accepted: 02/20/2024] [Indexed: 02/29/2024] Open
Abstract
Any kidney dimension and volume variation can be a remarkable indicator of kidney disorders. Precise kidney segmentation in standard planes plays an undeniable role in predicting kidney size and volume. On the other hand, ultrasound is the modality of choice in diagnostic procedures. This paper proposes a convolutional neural network with nested layers, namely Fast-Unet++, promoting the Fast and accurate Unet model. First, the model was trained and evaluated for segmenting sagittal and axial images of the kidney. Then, the predicted masks were used to estimate the kidney image biomarkers, including its volume and dimensions (length, width, thickness, and parenchymal thickness). Finally, the proposed model was tested on a publicly available dataset with various shapes and compared with the related networks. Moreover, the network was evaluated using a set of patients who had undergone ultrasound and computed tomography. The dice metric, Jaccard coefficient, and mean absolute distance were used to evaluate the segmentation step. 0.97, 0.94, and 3.23 mm for the sagittal frame, and 0.95, 0.9, and 3.87 mm for the axial frame were achieved. The kidney dimensions and volume were evaluated using accuracy, the area under the curve, sensitivity, specificity, precision, and F1.
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Affiliation(s)
| | - Seyed Morteza Bagheri
- Department of Radiology, Hasheminejad Kidney Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ali Shabanzadeh
- Research and Development Department, Med Fanavaran Plus Co., Karaj, Iran
| | - Mohammad Zare Mehrjardi
- Section of Body Imaging, Division of Clinical Research, Climax Radiology Education Foundation, Tehran, Iran
| | - Ardavan Akhavan
- Research and Development Department, Med Fanavaran Plus Co., Karaj, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Mostafa Taghipour
- Research and Development Department, Med Fanavaran Plus Co., Karaj, Iran
| | - Zahra Shabanzadeh
- Research and Development Department, Med Fanavaran Plus Co., Karaj, Iran
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5
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Zhang M, Ye Z, Yuan E, Lv X, Zhang Y, Tan Y, Xia C, Tang J, Huang J, Li Z. Imaging-based deep learning in kidney diseases: recent progress and future prospects. Insights Imaging 2024; 15:50. [PMID: 38360904 PMCID: PMC10869329 DOI: 10.1186/s13244-024-01636-5] [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: 11/19/2023] [Accepted: 01/27/2024] [Indexed: 02/17/2024] Open
Abstract
Kidney diseases result from various causes, which can generally be divided into neoplastic and non-neoplastic diseases. Deep learning based on medical imaging is an established methodology for further data mining and an evolving field of expertise, which provides the possibility for precise management of kidney diseases. Recently, imaging-based deep learning has been widely applied to many clinical scenarios of kidney diseases including organ segmentation, lesion detection, differential diagnosis, surgical planning, and prognosis prediction, which can provide support for disease diagnosis and management. In this review, we will introduce the basic methodology of imaging-based deep learning and its recent clinical applications in neoplastic and non-neoplastic kidney diseases. Additionally, we further discuss its current challenges and future prospects and conclude that achieving data balance, addressing heterogeneity, and managing data size remain challenges for imaging-based deep learning. Meanwhile, the interpretability of algorithms, ethical risks, and barriers of bias assessment are also issues that require consideration in future development. We hope to provide urologists, nephrologists, and radiologists with clear ideas about imaging-based deep learning and reveal its great potential in clinical practice.Critical relevance statement The wide clinical applications of imaging-based deep learning in kidney diseases can help doctors to diagnose, treat, and manage patients with neoplastic or non-neoplastic renal diseases.Key points• Imaging-based deep learning is widely applied to neoplastic and non-neoplastic renal diseases.• Imaging-based deep learning improves the accuracy of the delineation, diagnosis, and evaluation of kidney diseases.• The small dataset, various lesion sizes, and so on are still challenges for deep learning.
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Affiliation(s)
- Meng Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
- Medical Equipment Innovation Research Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
- Med+X Center for Manufacturing, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Enyu Yuan
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Xinyang Lv
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Yiteng Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Yuqi Tan
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Jing Tang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
| | - Jin Huang
- Medical Equipment Innovation Research Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
- Med+X Center for Manufacturing, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
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Park HC, Joo Y, Lee OJ, Lee K, Song TK, Choi C, Choi MH, Yoon C. Automated classification of liver fibrosis stages using ultrasound imaging. BMC Med Imaging 2024; 24:36. [PMID: 38321373 PMCID: PMC10848434 DOI: 10.1186/s12880-024-01209-4] [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: 09/14/2023] [Accepted: 01/21/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Ultrasound imaging is the most frequently performed for the patients with chronic hepatitis or liver cirrhosis. However, ultrasound imaging is highly operator dependent and interpretation of ultrasound images is subjective, thus well-trained radiologist is required for evaluation. Automated classification of liver fibrosis could alleviate the shortage of skilled radiologist especially in low-to-middle income countries. The purposed of this study is to evaluate deep convolutional neural networks (DCNNs) for classifying the degree of liver fibrosis according to the METAVIR score using US images. METHODS We used ultrasound (US) images from two tertiary university hospitals. A total of 7920 US images from 933 patients were used for training/validation of DCNNs. All patient were underwent liver biopsy or hepatectomy, and liver fibrosis was categorized based on pathology results using the METAVIR score. Five well-established DCNNs (VGGNet, ResNet, DenseNet, EfficientNet and ViT) was implemented to predict the METAVIR score. The performance of DCNNs for five-level (F0/F1/F2/F3/F4) classification was evaluated through area under the receiver operating characteristic curve (AUC) with 95% confidential interval, accuracy, sensitivity, specificity, positive and negative likelihood ratio. RESULTS Similar mean AUC values were achieved for five models; VGGNet (0.96), ResNet (0.96), DenseNet (0.95), EfficientNet (0.96), and ViT (0.95). The same mean accuracy (0.94) and specificity values (0.96) were yielded for all models. In terms of sensitivity, EffcientNet achieved highest mean value (0.85) while the other models produced slightly lower values range from 0.82 to 0.84. CONCLUSION In this study, we demonstrated that DCNNs can classify the staging of liver fibrosis according to METAVIR score with high performance using conventional B-mode images. Among them, EfficientNET that have fewer parameters and computation cost produced highest performance. From the results, we believe that DCNNs based classification of liver fibrosis may allow fast and accurate diagnosis of liver fibrosis without needs of additional equipment for add-on test and may be powerful tool for supporting radiologists in clinical practice.
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Grants
- NTIS Number: 9991007146 the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety
- HI21C0940110021 the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- No. 2022-0-00101 the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT)
- the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety
- the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT)
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Affiliation(s)
- Hyun-Cheol Park
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero, Yuseong-gu, 34047, Daejeon, Republic of Korea
| | - YunSang Joo
- Department of Computer Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu, 13120, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - O-Joun Lee
- Department of Artificial Intelligence, The Catholic University of Korea, 43, Jibong-ro, 14662, Bucheon-si, Gyeonggi-do, Republic of Korea
| | - Kunkyu Lee
- Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, 04107, Seoul, Republic of Korea
| | - Tai-Kyong Song
- Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, 04107, Seoul, Republic of Korea
| | - Chang Choi
- Department of Computer Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu, 13120, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Moon Hyung Choi
- Department of Radiology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seoul, Republic of Korea.
| | - Changhan Yoon
- Department of Biomedical Engineering, Department of Nanoscience and Engineering, Inje University, Inje-ro 197, 50834, Gimhae, Gyeongnam, Republic of Korea.
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Yang D, Tian C, Liu J, Peng Y, Xiong Z, Da J, Yang Y, Zha Y, Zeng X. Diffusion Tensor and Kurtosis MRI-Based Radiomics Analysis of Kidney Injury in Type 2 Diabetes. J Magn Reson Imaging 2024. [PMID: 38299753 DOI: 10.1002/jmri.29263] [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: 12/03/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can provide quantitative parameters that show promise for evaluation of diabetic kidney disease (DKD). The combination of radiomics with DTI and DKI may hold potential clinical value in detecting DKD. PURPOSE To investigate radiomics models of DKI and DTI for predicting DKD in type 2 diabetes mellitus (T2DM) and evaluate their performance in automated renal parenchyma segmentation. STUDY TYPE Prospective. POPULATION One hundred and sixty-three T2DM patients (87 DKD; 63 females; 27-80 years), randomly divided into training cohort (N = 114) and validation cohort (N = 49). FIELD STRENGTH/SEQUENCE 1.5-T, diffusion spectrum imaging (DSI) with 9 different b-values. ASSESSMENT The images of DSI were processed to generate DKI and DTI parameter maps, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). The Swin UNETR model was trained with 5-fold cross-validation using 100 samples for renal parenchyma segmentation. Subsequently, radiomics features were automatically extracted from each parameter map. The performance of the radiomics models on the validation cohort was evaluated by utilizing the receiver operating characteristic (ROC) curve. STATISTICAL TESTS Mann-Whitney U test, Chi-squared test, Pearson correlation coefficient, least absolute shrinkage and selection operator (LASSO), dice similarity coefficient (DSC), decision curve analysis (DCA), area under the curve (AUC), and DeLong's test. The threshold for statistical significance was set at P < 0.05. RESULTS The DKI_MD achieved the best segmentation performance (DSC, 0.925 ± 0.011). A combined radiomics model (DTI_FA, DTI_MD, DKI_FA, DKI_MD, and DKI_RD) showed the best performance (AUC, 0.918; 95% confidence interval [CI]: 0.820-0.991). When the threshold probability was greater than 20%, the combined model provided the greatest net benefit. Among the single parameter maps, the DTI_FA exhibited superior diagnostic performance (AUC, 887; 95% CI: 0.779-0.972). DATA CONCLUSION The radiomics signature constructed based on DKI and DTI may be used as an accurate and non-invasive tool to identify T2DM and DKD. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Daoyu Yang
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Chong Tian
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
- School of Medicine, Guizhou University, Guiyang, China
| | - Jian Liu
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Yunsong Peng
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Zhenliang Xiong
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Jingjing Da
- Renal Division, Department of Medicine, Guizhou Provincial People's Hospital, Guiyang, China
| | - Yuqi Yang
- Renal Division, Department of Medicine, Guizhou Provincial People's Hospital, Guiyang, China
| | - Yan Zha
- School of Medicine, Guizhou University, Guiyang, China
- Renal Division, Department of Medicine, Guizhou Provincial People's Hospital, Guiyang, China
| | - Xianchun Zeng
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
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Betzler BK, Chee EYL, He F, Lim CC, Ho J, Hamzah H, Tan NC, Liew G, McKay GJ, Hogg RE, Young IS, Cheng CY, Lim SC, Lee AY, Wong TY, Lee ML, Hsu W, Tan GSW, Sabanayagam C. Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes. J Am Med Inform Assoc 2023; 30:1904-1914. [PMID: 37659103 PMCID: PMC10654858 DOI: 10.1093/jamia/ocad179] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/17/2023] [Accepted: 08/21/2023] [Indexed: 09/04/2023] Open
Abstract
OBJECTIVE To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations. MATERIALS AND METHODS We trained 3 models: (1) image-only; (2) risk factor (RF)-only multivariable logistic regression (LR) model adjusted for age, sex, ethnicity, diabetes duration, HbA1c, systolic blood pressure; (3) hybrid multivariable LR model combining RF data and standardized z-scores from image-only model. Data from Singapore Integrated Diabetic Retinopathy Program (SiDRP) were used to develop (6066 participants with diabetes, primary-care-based) and internally validate (5-fold cross-validation) the models. External testing on 2 independent datasets: (1) Singapore Epidemiology of Eye Diseases (SEED) study (1885 participants with diabetes, population-based); (2) Singapore Macroangiopathy and Microvascular Reactivity in Type 2 Diabetes (SMART2D) (439 participants with diabetes, cross-sectional) in Singapore. Supplementary external testing on 2 Caucasian cohorts: (3) Australian Eye and Heart Study (AHES) (460 participants with diabetes, cross-sectional) and (4) Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) (265 participants with diabetes, cross-sectional). RESULTS In SiDRP validation, area under the curve (AUC) was 0.826(95% CI 0.818-0.833) for image-only, 0.847(0.840-0.854) for RF-only, and 0.866(0.859-0.872) for hybrid. Estimates with SEED were 0.764(0.743-0.785) for image-only, 0.802(0.783-0.822) for RF-only, and 0.828(0.810-0.846) for hybrid. In SMART2D, AUC was 0.726(0.686-0.765) for image-only, 0.701(0.660-0.741) in RF-only, 0.761(0.724-0.797) for hybrid. DISCUSSION AND CONCLUSION There is potential for DLA using retinal images as a screening adjunct for DKD among individuals with diabetes. This can value-add to existing DLA systems which diagnose diabetic retinopathy from retinal images, facilitating primary screening for DKD.
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Affiliation(s)
- Bjorn Kaijun Betzler
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Evelyn Yi Lyn Chee
- School of Computing, National University of Singapore, 117417, Singapore
| | - Feng He
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
| | - Cynthia Ciwei Lim
- Department of Renal Medicine, Singapore General Hospital, 168753, Singapore
| | - Jinyi Ho
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
| | - Haslina Hamzah
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
| | - Ngiap Chuan Tan
- SingHealth Polyclinics, Singapore Health Services, 168582, Singapore
| | - Gerald Liew
- Westmead Institute for Medical Research, University of Sydney, NSW 2145, Australia
| | - Gareth J McKay
- Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom
| | - Ruth E Hogg
- Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom
| | - Ian S Young
- Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
| | - Su Chi Lim
- Khoo Teck Puat Hospital, 768828, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, 117549, Singapore
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA 98104, United States
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
| | - Mong Li Lee
- School of Computing, National University of Singapore, 117417, Singapore
| | - Wynne Hsu
- School of Computing, National University of Singapore, 117417, Singapore
| | - Gavin Siew Wei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
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Shi S, Gao L, Zhang J, Zhang B, Xiao J, Xu W, Tian Y, Ni L, Wu X. The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients. BMC Med Inform Decis Mak 2023; 23:241. [PMID: 37904184 PMCID: PMC10617171 DOI: 10.1186/s12911-023-02343-9] [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: 06/14/2023] [Accepted: 10/16/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Diabetic kidney disease (DKD) has become the largest cause of end-stage kidney disease. Early and accurate detection of DKD is beneficial for patients. The present detection depends on the measurement of albuminuria or the estimated glomerular filtration rate, which is invasive and not optimal; therefore, new detection tools are urgently needed. Meanwhile, a close relationship between diabetic retinopathy and DKD has been reported; thus, we aimed to develop a novel detection algorithm for DKD using artificial intelligence technology based on retinal vascular parameters combined with several easily available clinical parameters in patients with type-2 diabetes. METHODS A total of 515 consecutive patients with type-2 diabetes mellitus from Xiangyang Central Hospital were included. Patients were stratified by DKD diagnosis and split randomly into either the training set (70%, N = 360) or the testing set (30%, N = 155) (random seed = 1). Data from the training set were used to develop the machine learning algorithm (MLA), while those from the testing set were used to validate the MLA. Model performances were evaluated. RESULTS The MLA using the random forest classifier presented optimal performance compared with other classifiers. When validated, the accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model were 84.5%(95% CI 83.3-85.7), 84.5%(82.3-86.7), 84.5%(82.7-86.3), 0.845(0.831-0.859), and 0.914(0.903-0.925), respectively. CONCLUSIONS A new machine learning algorithm for DKD diagnosis based on fundus images and 8 easily available clinical parameters was developed, which indicated that retinal vascular changes can assist in DKD screening and detection.
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Affiliation(s)
- Shaomin Shi
- Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China
| | - Ling Gao
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China
| | - Juan Zhang
- Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China
| | - Baifang Zhang
- Department of Biochemistry, Wuhan University TaiKang Medical School (School of Basic Medical Sciences), Wuhan, 430071, Hubei, China
| | - Jing Xiao
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China
| | - Wan Xu
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China
| | - Yuan Tian
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China.
| | - Lihua Ni
- Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China.
| | - Xiaoyan Wu
- Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China.
- Department of General Practice, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China.
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10
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Tsai MH, Jhou MJ, Liu TC, Fang YW, Lu CJ. An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages. Front Med (Lausanne) 2023; 10:1155426. [PMID: 37859858 PMCID: PMC10582636 DOI: 10.3389/fmed.2023.1155426] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/19/2023] [Indexed: 10/21/2023] Open
Abstract
Background and objectives Chronic kidney disease (CKD) is a global health concern. This study aims to identify key factors associated with renal function changes using the proposed machine learning and important variable selection (ML&IVS) scheme on longitudinal laboratory data. The goal is to predict changes in the estimated glomerular filtration rate (eGFR) in a cohort of patients with CKD stages 3-5. Design A retrospective cohort study. Setting and participants A total of 710 outpatients who presented with stable nondialysis-dependent CKD stages 3-5 at the Shin-Kong Wu Ho-Su Memorial Hospital Medical Center from 2016 to 2021. Methods This study analyzed trimonthly laboratory data including 47 indicators. The proposed scheme used stochastic gradient boosting, multivariate adaptive regression splines, random forest, eXtreme gradient boosting, and light gradient boosting machine algorithms to evaluate the important factors for predicting the results of the fourth eGFR examination, especially in patients with CKD stage 3 and those with CKD stages 4-5, with or without diabetes mellitus (DM). Main outcome measurement Subsequent eGFR level after three consecutive laboratory data assessments. Results Our ML&IVS scheme demonstrated superior predictive capabilities and identified significant factors contributing to renal function changes in various CKD groups. The latest levels of eGFR, blood urea nitrogen (BUN), proteinuria, sodium, and systolic blood pressure as well as mean levels of eGFR, BUN, proteinuria, and triglyceride were the top 10 significantly important factors for predicting the subsequent eGFR level in patients with CKD stages 3-5. In individuals with DM, the latest levels of BUN and proteinuria, mean levels of phosphate and proteinuria, and variations in diastolic blood pressure levels emerged as important factors for predicting the decline of renal function. In individuals without DM, all phosphate patterns and latest albumin levels were found to be key factors in the advanced CKD group. Moreover, proteinuria was identified as an important factor in the CKD stage 3 group without DM and CKD stages 4-5 group with DM. Conclusion The proposed scheme highlighted factors associated with renal function changes in different CKD conditions, offering valuable insights to physicians for raising awareness about renal function changes.
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Affiliation(s)
- Ming-Hsien Tsai
- Division of Nephrology, Department of Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- Department of Medicine, School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Yu-Wei Fang
- Division of Nephrology, Department of Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- Department of Medicine, School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan
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11
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Ha S, Choi SJ, Lee S, Wijaya RH, Kim JH, Joo EY, Kim JK. Predicting the Risk of Sleep Disorders Using a Machine Learning-Based Simple Questionnaire: Development and Validation Study. J Med Internet Res 2023; 25:e46520. [PMID: 37733411 PMCID: PMC10557018 DOI: 10.2196/46520] [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: 06/20/2023] [Accepted: 08/23/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Sleep disorders, such as obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia are common and can have serious health consequences. However, accurately diagnosing these conditions can be challenging as a result of the underrecognition of these diseases, the time-intensive nature of sleep monitoring necessary for a proper diagnosis, and patients' hesitancy to undergo demanding and costly overnight polysomnography tests. OBJECTIVE We aim to develop a machine learning algorithm that can accurately predict the risk of OSA, COMISA, and insomnia with a simple set of questions, without the need for a polysomnography test. METHODS We applied extreme gradient boosting to the data from 2 medical centers (n=4257 from Samsung Medical Center and n=365 from Ewha Womans University Medical Center Seoul Hospital). Features were selected based on feature importance calculated by the Shapley additive explanations (SHAP) method. We applied extreme gradient boosting using selected features to develop a simple questionnaire predicting sleep disorders (SLEEPS). The accuracy of the algorithm was evaluated using the area under the receiver operating characteristics curve. RESULTS In total, 9 features were selected to construct SLEEPS. SLEEPS showed high accuracy, with an area under the receiver operating characteristics curve of greater than 0.897 for all 3 sleep disorders, and consistent performance across both sets of data. We found that the distinction between COMISA and OSA was critical for accurate prediction. A publicly accessible website was created based on the algorithm that provides predictions for the risk of the 3 sleep disorders and shows how the risk changes with changes in weight or age. CONCLUSIONS SLEEPS has the potential to improve the diagnosis and treatment of sleep disorders by providing more accessibility and convenience. The creation of a publicly accessible website based on the algorithm provides a user-friendly tool for assessing the risk of OSA, COMISA, and insomnia.
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Affiliation(s)
- Seokmin Ha
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Su Jung Choi
- Graduate School of Clinical Nursing Science, Sungkyunkwan University, Seoul, Republic of Korea
| | - Sujin Lee
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Reinatt Hansel Wijaya
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jee Hyun Kim
- Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jae Kyoung Kim
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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12
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Wang F, Mao R, Yan L, Ling S, Cai Z. A deep learning-based approach for rectus abdominis segmentation and distance measurement in ultrasonography. Front Physiol 2023; 14:1246994. [PMID: 37736487 PMCID: PMC10509763 DOI: 10.3389/fphys.2023.1246994] [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: 06/25/2023] [Accepted: 08/22/2023] [Indexed: 09/23/2023] Open
Abstract
Introduction: Diastasis recti abdominis (DRA) is a common condition in postpartum women. Measuring the distance between separated rectus abdominis (RA) in ultrasound images is a reliable method for the diagnosis of this disease. In clinical practice, the RA distance in multiple ultrasound images of a patient is measured by experienced sonographers, which is time-consuming, labor-intensive, and highly dependent on experience of operators. Therefore, an objective and fully automatic technique is highly desired to improve the DRA diagnostic efficiency. This study aimed to demonstrate the deep learning-based methods on the performance of RA segmentation and distance measurement in ultrasound images. Methods: A total of 675 RA ultrasound images were collected from 94 postpartum women, and were split into training (448 images), validation (86 images), and test (141 images) datasets. Three segmentation models including U-Net, UNet++ and Res-UNet were evaluated on their performance of RA segmentation and distance measurement. Results: Res-UNet model outperformed the other two models with the highest Dice score (85.93% ± 0.26%), the highest MIoU score (76.00% ± 0.39%) and the lowest Hausdorff distance (21.80 ± 0.76 mm). The average physical distance between RAs measured from the segmentation masks generated by Res-UNet and that measured by experienced sonographers was only 3.44 ± 0.16 mm. In addition, these two measurements were highly correlated with each other (r = 0.944), with no systematic difference. Conclusion: Deep learning model Res-UNet has good reliability in RA segmentation and distance measurement in ultrasound images, with great potential in the clinical diagnosis of DRA.
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Affiliation(s)
- Fei Wang
- Center of Four-Dimensional Ultrasound, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Rongsong Mao
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Laifa Yan
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Shan Ling
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Zhenyu Cai
- Department of Ultrasound, Zhejiang Medical and Health Group Hangzhou Hospital, Hangzhou, Zhejiang, China
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13
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Jain Y, Godwin LL, Ju Y, Sood N, Quardokus EM, Bueckle A, Longacre T, Horning A, Lin Y, Esplin ED, Hickey JW, Snyder MP, Patterson NH, Spraggins JM, Börner K. Segmentation of human functional tissue units in support of a Human Reference Atlas. Commun Biol 2023; 6:717. [PMID: 37468557 PMCID: PMC10356924 DOI: 10.1038/s42003-023-04848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/17/2023] [Indexed: 07/21/2023] Open
Abstract
The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. Tom was added to the HuBMAP infrastructure to run kidney FTU segmentation at scale-showcasing the value of Kaggle competitions for advancing research.
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Affiliation(s)
- Yashvardhan Jain
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
| | - Leah L Godwin
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Yingnan Ju
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Naveksha Sood
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Andreas Bueckle
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Teri Longacre
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Aaron Horning
- Thermo Fisher Scientific, South San Francisco, CA, 94080, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Edward D Esplin
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, 37232, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, 37232, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
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14
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Zhao D, Wang W, Tang T, Zhang YY, Yu C. Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review. Comput Struct Biotechnol J 2023; 21:3315-3326. [PMID: 37333860 PMCID: PMC10275698 DOI: 10.1016/j.csbj.2023.05.029] [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: 10/28/2022] [Revised: 05/28/2023] [Accepted: 05/28/2023] [Indexed: 06/20/2023] Open
Abstract
Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.
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Affiliation(s)
- Dan Zhao
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Wei Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Tian Tang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Ying-Ying Zhang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Chen Yu
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
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15
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Singla R, Hu R, Ringstrom C, Lessoway V, Reid J, Nguan C, Rohling R. The Kidneys Are Not All Normal: Transplanted Kidneys and Their Speckle Distributions. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1268-1274. [PMID: 36842904 DOI: 10.1016/j.ultrasmedbio.2023.01.013] [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: 09/13/2022] [Revised: 12/21/2022] [Accepted: 01/19/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVE Modelling ultrasound speckle to characterise tissue properties has generated considerable interest. As speckle is dependent on the underlying tissue architecture, modelling it may aid in tasks such as segmentation or disease detection. For the transplanted kidney, where ultrasound is used to investigate dysfunction, it is unknown which statistical distribution best characterises such speckle. This applies to the regions of the transplanted kidney: the cortex, the medulla and the central echogenic complex. Furthermore, it is unclear how these distributions vary by patient variables such as age, sex, body mass index, primary disease or donor type. These traits may influence speckle modelling given their influence on kidney anatomy. We investigate these two aims. METHODS B-mode images from n = 821 kidney transplant recipients (one image per recipient) were automatically segmented into the cortex, medulla and central echogenic complex using a neural network. Seven distinct probability distributions were fitted to each region's histogram, and statistical analysis was performed. DISCUSSION The Rayleigh and Nakagami distributions had model parameters that differed significantly between the three regions (p ≤ 0.05). Although both had excellent goodness of fit, the Nakagami had higher Kullbeck-Leibler divergence. Recipient age correlated weakly with scale in the cortex (Ω: ρ = 0.11, p = 0.004), while body mass index correlated weakly with shape in the medulla (m: ρ = 0.08, p = 0.04). Neither sex, primary disease nor donor type exhibited any correlation. CONCLUSION We propose the Nakagami distribution be used to characterize transplanted kidneys regionally independent of disease etiology and most patient characteristics.
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Affiliation(s)
- Rohit Singla
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Ricky Hu
- Faculty of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Cailin Ringstrom
- Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Victoria Lessoway
- Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Janice Reid
- Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Christopher Nguan
- Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Robert Rohling
- Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada; Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
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16
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Cho H, Song I, Jang J, Yoo Y. A Lightweight Deep Learning Network on a System-on-Chip for Wearable Ultrasound Bladder Volume Measurement Systems: Preliminary Study. Bioengineering (Basel) 2023; 10:bioengineering10050525. [PMID: 37237594 DOI: 10.3390/bioengineering10050525] [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: 03/29/2023] [Revised: 04/17/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Bladder volume assessments are crucial for managing urinary disorders. Ultrasound imaging (US) is a preferred noninvasive, cost-effective imaging modality for bladder observation and volume measurements. However, the high operator dependency of US is a major challenge due to the difficulty in evaluating ultrasound images without professional expertise. To address this issue, image-based automatic bladder volume estimation methods have been introduced, but most conventional methods require high-complexity computing resources that are not available in point-of-care (POC) settings. Therefore, in this study, a deep learning-based bladder volume measurement system was developed for POC settings using a lightweight convolutional neural network (CNN)-based segmentation model, which was optimized on a low-resource system-on-chip (SoC) to detect and segment the bladder region in ultrasound images in real time. The proposed model achieved high accuracy and robustness and can be executed on the low-resource SoC at 7.93 frames per second, which is 13.44 times faster than the frame rate of a conventional network with negligible accuracy drawbacks (0.004 of the Dice coefficient). The feasibility of the developed lightweight deep learning network was demonstrated using tissue-mimicking phantoms.
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Affiliation(s)
- Hyunwoo Cho
- Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea
| | - Ilseob Song
- Medical Solutions Institute, Sogang University, Seoul 04107, Republic of Korea
- Edgecare Inc., TE1103, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Republic of Korea
| | - Jihun Jang
- Medical Solutions Institute, Sogang University, Seoul 04107, Republic of Korea
- Edgecare Inc., TE1103, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Republic of Korea
| | - Yangmo Yoo
- Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea
- Edgecare Inc., TE1103, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Republic of Korea
- Department of Biomedical Engineering, Sogang University, Seoul 04107, Republic of Korea
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17
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Bala D, Hossain MS, Hossain MA, Abdullah MI, Rahman MM, Manavalan B, Gu N, Islam MS, Huang Z. MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification. Neural Netw 2023; 161:757-775. [PMID: 36848828 PMCID: PMC9943560 DOI: 10.1016/j.neunet.2023.02.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 02/05/2023] [Accepted: 02/14/2023] [Indexed: 02/23/2023]
Abstract
The monkeypox virus poses a new pandemic threat while we are still recovering from COVID-19. Despite the fact that monkeypox is not as lethal and contagious as COVID-19, new patient cases are recorded every day. If preparations are not made, a global pandemic is likely. Deep learning (DL) techniques are now showing promise in medical imaging for figuring out what diseases a person has. The monkeypox virus-infected human skin and the region of the skin can be used to diagnose the monkeypox early because an image has been used to learn more about the disease. But there is still no reliable Monkeypox database that is available to the public that can be used to train and test DL models. As a result, it is essential to collect images of monkeypox patients. The "MSID" dataset, short form of "Monkeypox Skin Images Dataset", which was developed for this research, is free to use and can be downloaded from the Mendeley Data database by anyone who wants to use it. DL models can be built and used with more confidence using the images in this dataset. These images come from a variety of open-source and online sources and can be used for research purposes without any restrictions. Furthermore, we proposed and evaluated a modified DenseNet-201 deep learning-based CNN model named MonkeyNet. Using the original and augmented datasets, this study suggested a deep convolutional neural network that was able to correctly identify monkeypox disease with an accuracy of 93.19% and 98.91% respectively. This implementation also shows the Grad-CAM which indicates the level of the model's effectiveness and identifies the infected regions in each class image, which will help the clinicians. The proposed model will also help doctors make accurate early diagnoses of monkeypox disease and protect against the spread of the disease.
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Affiliation(s)
- Diponkor Bala
- Department of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh; Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea.
| | - Md Shamim Hossain
- School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, Anhui, 230026, China.
| | | | - Md Ibrahim Abdullah
- Department of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh.
| | - Md Mizanur Rahman
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia.
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea.
| | - Naijie Gu
- School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, Anhui, 230026, China.
| | - Mohammad S Islam
- School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS), 15 Broadway, Ultimo, NSW 2007, Australia.
| | - Zhangjin Huang
- School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, Anhui, 230026, China; USTC-Deqing Alpha Innovation Institute, Huzhou, 313299, China.
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18
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A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation. Comput Biol Med 2023; 157:106726. [PMID: 36924732 DOI: 10.1016/j.compbiomed.2023.106726] [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: 11/15/2022] [Revised: 02/07/2023] [Accepted: 02/27/2023] [Indexed: 03/05/2023]
Abstract
Deep learning-based methods have become the dominant methodology in medical image processing with the advancement of deep learning in natural image classification, detection, and segmentation. Deep learning-based approaches have proven to be quite effective in single lesion recognition and segmentation. Multiple-lesion recognition is more difficult than single-lesion recognition due to the little variation between lesions or the too wide range of lesions involved. Several studies have recently explored deep learning-based algorithms to solve the multiple-lesion recognition challenge. This paper includes an in-depth overview and analysis of deep learning-based methods for multiple-lesion recognition developed in recent years, including multiple-lesion recognition in diverse body areas and recognition of whole-body multiple diseases. We discuss the challenges that still persist in the multiple-lesion recognition tasks by critically assessing these efforts. Finally, we outline existing problems and potential future research areas, with the hope that this review will help researchers in developing future approaches that will drive additional advances.
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19
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Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol 2023; 36:1101-1117. [PMID: 36786976 PMCID: PMC10227138 DOI: 10.1007/s40620-023-01573-4] [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: 08/06/2022] [Accepted: 01/01/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVES In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management. METHODS We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. RESULTS From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context. CONCLUSIONS Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.
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Mo X, Chen W, Chen S, Chen Z, Guo Y, Chen Y, Wu X, Zhang L, Chen Q, Jin Z, Li M, Chen L, You J, Xiong Z, Zhang B, Zhang S. MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study. Insights Imaging 2023; 14:28. [PMID: 36746892 PMCID: PMC9902579 DOI: 10.1186/s13244-023-01370-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 01/03/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function. METHODS A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models. RESULTS The models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935-0.940), 0.919 (95%CI 0.916-0.922), and 0.959 (95%CI 0.956-0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800-0.807), 0.852 (95%CI 0.846-0.857), and 0.863 (95%CI 0.857-0.887) in the validation cohorts, respectively. CONCLUSION We developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.
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Affiliation(s)
- Xiaokai Mo
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Wenbo Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China ,grid.470066.3Department of Radiology, Huizhou Municipal Central Hospital, No. 41 Eling Bei Road, Huizhou, 516001 Guangdong People’s Republic of China
| | - Simin Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhuozhi Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Yuanshu Guo
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Yulian Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Xuewei Wu
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Lu Zhang
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Qiuying Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhe Jin
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Minmin Li
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Luyan Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Jingjing You
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhiyuan Xiong
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China.
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China.
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Snider EJ, Hernandez-Torres SI, Hennessey R. Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting. Diagnostics (Basel) 2023; 13:diagnostics13030417. [PMID: 36766522 PMCID: PMC9914871 DOI: 10.3390/diagnostics13030417] [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: 11/30/2022] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 01/26/2023] Open
Abstract
Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network-termed ShrapML-blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.
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Lin Z, Yang W, Zhang W, Jiang C, Chu J, Yang J, Yuan X. Recognizing pathology of renal tumor from macroscopic cross-section image by deep learning. Biomed Eng Online 2023; 22:3. [PMID: 36670469 PMCID: PMC9854121 DOI: 10.1186/s12938-023-01064-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 01/09/2023] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVES This study aims to develop and evaluate the deep learning-based classification model for recognizing the pathology of renal tumor from macroscopic cross-section image. METHODS A total of 467 pathology-confirmed patients who received radical nephrectomy or partial nephrectomy were retrospectively enrolled. The experiment of distinguishing malignant and benign renal tumor are conducted followed by performing the multi-subtypes classification models for recognizing four subtypes of benign tumor and four subtypes of malignant tumors, respectively. The classification models used the same backbone networks which are based on the convolutional neural network (CNN), including EfficientNet-B4, ResNet-18, and VGG-16. The performance of the classification models was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Besides, we performed the quantitative comparison among these CNN models. RESULTS For the model to differentiate the malignant tumor from the benign tumor, three CNN models all obtained relatively satisfactory performance and the highest AUC was achieved by the ResNet-18 model (AUC = 0.9226). There is not statistically significance between EfficientNet-B4 and ResNet-18 architectures and both of them are significantly statistically better than the VGG-16 model. The micro-averaged AUC, macro-averaged sensitivity, macro-averaged specificity, and micro-averaged accuracy for the VGG-16 model to distinguish the malignant tumor subtypes achieved 0.9398, 0.5774, 0.8660, and 0.7917, respectively. The performance of the EfficientNet-B4 is not better than that of VGG-16 in terms of micro-averaged AUC except for other metrics. For the models to recognize the benign tumor subtypes, the EfficientNet-B4 ranked the best performance, but had no significantly statistical difference with other two models with respect to micro-averaged AUC. CONCLUSIONS The classification results were relatively satisfactory, which showed the potential for clinical application when analyzing the renal tumor macroscopic cross-section images. Automatically distinguishing the malignant tumor from benign tumor and identifying the subtypes pathology of renal tumor could make the patient-management process more efficient.
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Affiliation(s)
- Zefang Lin
- grid.258164.c0000 0004 1790 3548Zhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
| | - Weihong Yang
- grid.258164.c0000 0004 1790 3548Department of Medical Equipment Engineering, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
| | - Wenqiang Zhang
- grid.258164.c0000 0004 1790 3548Department of Urology, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
| | - Chao Jiang
- Nursing Department, Guizhou Aerospace Hospital, Zunyi, China
| | - Jing Chu
- grid.258164.c0000 0004 1790 3548Department of Urology, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
| | - Jing Yang
- grid.258164.c0000 0004 1790 3548Department of Pathology, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
| | - Xiaoxu Yuan
- grid.258164.c0000 0004 1790 3548Department of Urology, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
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Chen W, Zhang L, Cai G, Zhang B, Lian Z, Li J, Wang W, Zhang Y, Mo X. Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study. Front Endocrinol (Lausanne) 2023; 14:1050078. [PMID: 37139339 PMCID: PMC10150993 DOI: 10.3389/fendo.2023.1050078] [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: 10/12/2022] [Accepted: 02/28/2023] [Indexed: 05/05/2023] Open
Abstract
Introduction Diabetic nephropathy (DN) has become a major public health burden in China. A more stable method is needed to reflect the different stages of renal function impairment. We aimed to determine the possible practicability of machine learning (ML)-based multimodal MRI texture analysis (mMRI-TA) for assessing renal function in DN. Methods For this retrospective study, 70 patients (between 1 January 2013 and 1 January 2020) were included and randomly assigned to the training cohort (n1 = 49) and the testing cohort (n2 = 21). According to the estimated glomerular filtration rate (eGFR), patients were assigned into the normal renal function (normal-RF) group, the non-severe renal function impairment (non-sRI) group, and the severe renal function impairment (sRI) group. Based on the largest coronal image of T2WI, the speeded up robust features (SURF) algorithm was used for texture feature extraction. Analysis of variance (ANOVA) and relief and recursive feature elimination (RFE) were applied to select the important features and then support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms were used for the model construction. The values of area under the curve (AUC) on receiver operating characteristic (ROC) curve analysis were used to assess their performance. The robust T2WI model was selected to construct a multimodal MRI model by combining the measured BOLD (blood oxygenation level-dependent) and diffusion-weighted imaging (DWI) values. Results The mMRI-TA model achieved robust and excellent performance in classifying the sRI group, non-sRI group, and normal-RF group, with an AUC of 0.978 (95% confidence interval [CI]: 0.963, 0.993), 0.852 (95% CI: 0.798, 0.902), and 0.972 (95% CI: 0.995, 1.000), respectively, in the training cohort and 0.961 (95% CI: 0.853, 1.000), 0.809 (95% CI: 0.600, 0.980), and 0.850 (95% CI: 0.638, 0.988), respectively, in the testing cohort. Discussion The model built from multimodal MRI on DN outperformed other models in assessing renal function and fibrosis. Compared to the single T2WI sequence, mMRI-TA can improve the performance in assessing renal function.
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Affiliation(s)
- Wenbo Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
- Department of Radiology, Huizhou Central People’s Hospital, Huizhou, Guangdong, China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Guanhui Cai
- Department of Radiology, Huizhou Central People’s Hospital, Huizhou, Guangdong, China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Zhouyang Lian
- Department of Radiology, Guandong Academy of Medical Sciences/Guangdong Provincial People’s Hospital, Guangzhou, Guangdong, China
| | - Jing Li
- Division of Nephrology, Guangdong Academy of Medical Sciences/Guangdong Provincial People’s Hospital, Guangzhou, Guangdong, China
| | - Wenjian Wang
- Division of Nephrology, Guangdong Academy of Medical Sciences/Guangdong Provincial People’s Hospital, Guangzhou, Guangdong, China
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
- *Correspondence: Xiaokai Mo, ; Yuxian Zhang, ; Wenjian Wang,
| | - Yuxian Zhang
- Department of Nuclear Medicine, ZhuJiang Hospital of Southern Medical University, Guangzhou, Guangdong, China
- *Correspondence: Xiaokai Mo, ; Yuxian Zhang, ; Wenjian Wang,
| | - Xiaokai Mo
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
- *Correspondence: Xiaokai Mo, ; Yuxian Zhang, ; Wenjian Wang,
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Filler G, Gipson DS, Iyamuremye D, Díaz González de Ferris ME. Artificial Intelligence in Pediatric Nephrology-A Call for Action. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:17-24. [PMID: 36723276 DOI: 10.1053/j.akdh.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/24/2022] [Accepted: 11/07/2022] [Indexed: 12/24/2022]
Abstract
Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient management. In adult-focused nephrology, artificial intelligence is beginning to be used to improve clinical care, hemodialysis prescriptions, and follow-up of transplant recipients. This article provides an overview of medical artificial intelligence applications relevant to pediatric nephrology. We describe the core concepts of artificial intelligence and machine learning and cover the basics of neural networks and deep learning. We also discuss some examples for clinical applications of artificial intelligence in pediatric nephrology, including neonatal kidney function, early recognition of acute kidney injury, renally cleared drug dosing, intrapatient variability, urinary tract infection workup in infancy, and longitudinal disease progression. Furthermore, we consider the future of artificial intelligence in clinical pediatric nephrology and its potential impact on medical practice and address the ethical issues artificial intelligence raises in terms of clinical decision-making, health care provider-patient relationship, patient privacy, and data collection. This article also represents a call for action involving those of us striving to provide optimal services for children, adolescents, and young adults with chronic conditions.
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Affiliation(s)
- Guido Filler
- Division of Pediatric Nephrology, Departments of Paediatrics, Western University, London, Ontario, Canada; Departments of Medicine, Western University, London, Ontario, Canada; Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada.
| | - Debbie S Gipson
- Department of Pediatrics, University of Michigan, Ann Arbor, Michigan
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Chronic kidney disease and NLRP3 inflammasome: Pathogenesis, development and targeted therapeutic strategies. Biochem Biophys Rep 2022; 33:101417. [PMID: 36620089 PMCID: PMC9813680 DOI: 10.1016/j.bbrep.2022.101417] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 12/27/2022] Open
Abstract
Chronic kidney disease (CKD) is a global health concern and public health priority. The condition often involves inflammation due to the accumulation of toxins and the reduced clearance of inflammatory cytokines, leading to gradual loss of kidney function. Because of the tremendous burden of CKD, finding effective treatment strategies against inflammation is crucial. Substantial evidence suggests an association between kidney disease and the inflammasome. As a well-known multiprotein signaling complex, the NLR family pyrin domain containing 3 (NLRP3) inflammasome plays an important role in inducing renal inflammation and fibrosis. Small molecule inhibitors targeting the NLRP3 inflammasome are potential agents for the treatment of CKD.The NLRP3 inflammasome activation amplifies the inflammation response, promoting pyroptotic cell death. Thus, it may contribute to the onset and progression of CKD, but the mechanism behind inflammasome activation in CKD remains obscure.In this review, we summarized recent findings on the role of the NLRP3 inflammasome in CKD and new strategies targeting the NLRP3 inflammasome.
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Key Words
- ,IL-18, Interleukin-18
- ASC, apoptosis-associated speck-like protein
- Ang II, Angiotensin II
- CKD, Chronic kidney disease
- Chronic kidney disease
- DAMPs, damage-associated molecular patterns
- ESRD, End-stage renal disease
- GFR, glomerular filtration rate
- HK-2, renal tubular epithelial cells
- IL-1β, Interleukin-1β
- Inflammasome
- Kidney function
- LRR, leucine-rich repeat
- NEK7, NIMA-related kinase 7
- NF-kB, nuclear factor kappa-B
- NLRP3, NLR family pyrin domain containing 3
- NOD-like receptor
- PAMPs, Pathogen-associated molecular patterns
- ROS, reactive oxygen species
- TXNIP, thioredoxin-interacting protein
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Santos R, Bürgi M, Mateos JM, Luciani A, Loffing J. Too bright for 2 dimensions: recent progress in advanced 3-dimensional microscopy of the kidney. Kidney Int 2022; 102:1238-1246. [PMID: 35963448 DOI: 10.1016/j.kint.2022.06.031] [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: 03/19/2022] [Revised: 06/19/2022] [Accepted: 06/24/2022] [Indexed: 01/12/2023]
Abstract
The kidney is a structurally and functionally complex organ responsible for the control of water, ion, and other solute homeostasis. Moreover, the kidneys excrete metabolic waste products and produce hormones, such as renin and erythropoietin. The functional unit of the kidney is the nephron, which is composed by a serial arrangement of a filter unit called the renal corpuscle and several tubular segments that modulate the filtered fluid by reabsorption and secretion. Within each kidney, thousands of nephrons are closely intermingled and surrounded by an intricate network of blood vessels and various interstitial cell types, including fibroblasts and immune cells. This complex tissue architecture is essential for proper kidney function. In fact, kidney disease is often reflected or even caused by a derangement of the histologic structures. Frequently, kidney histology is studied using microscopic analysis of 2-dimensional tissue sections, which, however, misses important 3-dimensional spatial information. Reconstruction of serial sections tries to overcome this limitation, but is technically challenging, time-consuming, and often inherently linked to sectioning artifacts. In recent years, advances in tissue preparation (e.g., optical clearing) and new light- and electron-microscopic methods have provided novel avenues for 3-dimensional kidney imaging. Combined with novel machine-learning algorithms, these approaches offer unprecedented options for large-scale and automated analysis of kidney structure and function. This review provides a brief overview of these emerging imaging technologies and presents key examples of how these approaches are already used to study the normal and the diseased kidney.
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Affiliation(s)
- Rui Santos
- Institute of Anatomy, University of Zurich, Zurich, Switzerland
| | - Max Bürgi
- Institute of Anatomy, University of Zurich, Zurich, Switzerland
| | - José María Mateos
- Centre for Microscopy and Image Analysis, University of Zurich, Zurich, Switzerland
| | - Alessandro Luciani
- Institute of Physiology, University of Zurich, Zurich, Switzerland; National Centre of Competence in Research "Kidney.CH," University of Zurich, Zurich, Switzerland
| | - Johannes Loffing
- Institute of Anatomy, University of Zurich, Zurich, Switzerland; National Centre of Competence in Research "Kidney.CH," University of Zurich, Zurich, Switzerland.
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Tsai MC, Lu HHS, Chang YC, Huang YC, Fu LS. Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach. JMIR Med Inform 2022; 10:e40878. [PMID: 36322109 PMCID: PMC9669887 DOI: 10.2196/40878] [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: 07/12/2022] [Revised: 09/16/2022] [Accepted: 10/02/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND In recent years, the progress and generalization surrounding portable ultrasonic probes has made ultrasound (US) a useful tool for physicians when making a diagnosis. With the advent of machine learning and deep learning, the development of a computer-aided diagnostic system for screening renal US abnormalities can assist general practitioners in the early detection of pediatric kidney diseases. OBJECTIVE In this paper, we sought to evaluate the diagnostic performance of deep learning techniques to classify kidney images as normal and abnormal. METHODS We chose 330 normal and 1269 abnormal pediatric renal US images for establishing a model for artificial intelligence. The abnormal images involved stones, cysts, hyperechogenicity, space-occupying lesions, and hydronephrosis. We performed preprocessing of the original images for subsequent deep learning. We redefined the final connecting layers for classification of the extracted features as abnormal or normal from the ResNet-50 pretrained model. The performances of the model were tested by a validation data set using area under the receiver operating characteristic curve, accuracy, specificity, and sensitivity. RESULTS The deep learning model, 94 MB parameters in size, based on ResNet-50, was built for classifying normal and abnormal images. The accuracy, (%)/area under curve, of the validated images of stone, cyst, hyperechogenicity, space-occupying lesions, and hydronephrosis were 93.2/0.973, 91.6/0.940, 89.9/0.940, 91.3/0.934, and 94.1/0.996, respectively. The accuracy of normal image classification in the validation data set was 90.1%. Overall accuracy of (%)/area under curve was 92.9/0.959.. CONCLUSIONS We established a useful, computer-aided model for automatic classification of pediatric renal US images in terms of normal and abnormal categories.
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Affiliation(s)
- Ming-Chin Tsai
- Department of Pediatrics, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsing-chu, Taiwan
| | - Yueh-Chuan Chang
- Institute of Electrical & Control Engineering, National Yang Ming Chiao Tung University, Hsing-chu, Taiwan
| | - Yung-Chieh Huang
- Department of Pediatrics, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Pediatrics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Lin-Shien Fu
- Department of Pediatrics, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Pediatrics, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
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Schena FP, Magistroni R, Narducci F, Abbrescia DI, Anelli VW, Di Noia T. Artificial intelligence in glomerular diseases. Pediatr Nephrol 2022; 37:2533-2545. [PMID: 35266037 DOI: 10.1007/s00467-021-05419-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 11/30/2022]
Abstract
In this narrative review, we focus on the application of artificial intelligence in the clinical history of patients with glomerular disease, digital pathology in kidney biopsy, renal ultrasonography imaging, and prediction of chronic kidney disease (CKD). With the development of natural language processing, the clinical history of a patient can be used to identify a computable phenotype. In kidney pathology, digital imaging has adopted innovative deep learning algorithms (DLAs) that can improve the predictive capability of the examined lesions. However, at this time, these applications can only be used in research because there is no recognized validation to replace the conventional diagnostic applications. Kidney ultrasonography, used in the clinical examination of patients, provides information about the progression of kidney damage. Machine learning algorithms (MLAs) with promising results for the early detection of CKD have been proposed, but, still, they are not solid enough to be incorporated into the clinical practice. A few tools for glomerulonephritis, based on MLAs, are available in clinical practice. They can be downloaded on computers and cellular phones but can only be applied to uniracial cohorts of patients. To improve their performance, it is necessary to organize large consortia with multiracial cohorts. Finally, in many studies MLA development has been carried out using retrospective cohorts. The performance of the models might differ in retrospective cohorts compared to real-world data. Therefore, the models should be validated in prospective external large cohorts.
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Affiliation(s)
- Francesco P Schena
- Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy.
| | | | - Fedelucio Narducci
- Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy
| | | | - Vito W Anelli
- Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy
| | - Tommaso Di Noia
- Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy
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Mumuni AN, Hasford F, Udeme NI, Dada MO, Awojoyogbe BO. A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2022-0121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Abstract
Diagnostic imaging (DI) refers to techniques and methods of creating images of the body’s internal parts and organs with or without the use of ionizing radiation, for purposes of diagnosing, monitoring and characterizing diseases. By default, DI equipment are technology based and in recent times, there has been widespread automation of DI operations in high-income countries while low and middle-income countries (LMICs) are yet to gain traction in automated DI. Advanced DI techniques employ artificial intelligence (AI) protocols to enable imaging equipment perceive data more accurately than humans do, and yet automatically or under expert evaluation, make clinical decisions such as diagnosis and characterization of diseases. In this narrative review, SWOT analysis is used to examine the strengths, weaknesses, opportunities and threats associated with the deployment of AI-based DI protocols in LMICs. Drawing from this analysis, a case is then made to justify the need for widespread AI applications in DI in resource-poor settings. Among other strengths discussed, AI-based DI systems could enhance accuracies in diagnosis, monitoring, characterization of diseases and offer efficient image acquisition, processing, segmentation and analysis procedures, but may have weaknesses regarding the need for big data, huge initial and maintenance costs, and inadequate technical expertise of professionals. They present opportunities for synthetic modality transfer, increased access to imaging services, and protocol optimization; and threats of input training data biases, lack of regulatory frameworks and perceived fear of job losses among DI professionals. The analysis showed that successful integration of AI in DI procedures could position LMICs towards achievement of universal health coverage by 2030/2035. LMICs will however have to learn from the experiences of advanced settings, train critical staff in relevant areas of AI and proceed to develop in-house AI systems with all relevant stakeholders onboard.
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Affiliation(s)
| | - Francis Hasford
- Department of Medical Physics , University of Ghana, Ghana Atomic Energy Commission , Accra , Ghana
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Hsu FR, Dai ST, Chou CM, Huang SY. The application of artificial intelligence to support biliary atresia screening by ultrasound images: A study based on deep learning models. PLoS One 2022; 17:e0276278. [PMID: 36260613 PMCID: PMC9581370 DOI: 10.1371/journal.pone.0276278] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 10/03/2022] [Indexed: 11/09/2022] Open
Abstract
Purpose Early confirmation or ruling out biliary atresia (BA) is essential for infants with delayed onset of jaundice. In the current practice, percutaneous liver biopsy and intraoperative cholangiography (IOC) remain the golden standards for diagnosis. In Taiwan, the diagnostic methods are invasive and can only be performed in selective medical centers. However, referrals from primary physicians and local pediatricians are often delayed because of lacking clinical suspicions. Ultrasounds (US) are common screening tools in local hospitals and clinics, but the pediatric hepatobiliary US particularly requires well-trained imaging personnel. The meaningful comprehension of US is highly dependent on individual experience. For screening BA through human observation on US images, the reported sensitivity and specificity were achieved by pediatric radiologists, pediatric hepatobiliary experts, or pediatric surgeons. Therefore, this research developed a tool based on deep learning models for screening BA to assist pediatric US image reading by general physicians and pediatricians. Methods De-identified hepatobiliary US images of 180 patients from Taichung Veterans General Hospital were retrospectively collected under the approval of the Institutional Review Board. Herein, the top network models of ImageNet Large Scale Visual Recognition Competition and other network models commonly used for US image recognition were included for further study to classify US images as BA or non-BA. The performance of different network models was expressed by the confusion matrix and receiver operating characteristic curve. There were two methods proposed to solve disagreement by US image classification of a single patient. The first and second methods were the positive-dominance law and threshold law. During the study, the US images of three successive patients suspected to have BA were classified by the trained models. Results Among all included patients contributing US images, 41 patients were diagnosed with BA by surgical intervention and 139 patients were either healthy controls or had non-BA diagnoses. In this study, a total of 1,976 original US images were enrolled. Among them, 417 and 1,559 raw images were from patients with BA and without BA, respectively. Meanwhile, ShuffleNet achieved the highest accuracy of 90.56% using the same training parameters as compared with other network models. The sensitivity and specificity were 67.83% and 96.76%, respectively. In addition, the undesired false-negative prediction was prevented by applying positive-dominance law to interpret different images of a single patient with an acceptable false-positive rate, which was 13.64%. For the three consecutive patients with delayed obstructive jaundice with IOC confirmed diagnoses, ShuffleNet achieved accurate diagnoses in two patients. Conclusion The current study provides a screening tool for identifying possible BA by hepatobiliary US images. The method was not designed to replace liver biopsy or IOC, but to decrease human error for interpretations of US. By applying the positive-dominance law to ShuffleNet, the false-negative rate and the specificities were 0 and 86.36%, respectively. The trained deep learning models could aid physicians other than pediatric surgeons, pediatric gastroenterologists, or pediatric radiologists, to prevent misreading pediatric hepatobiliary US images. The current artificial intelligence (AI) tool is helpful for screening BA in the real world.
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Affiliation(s)
- Fang-Rong Hsu
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Taiwan
| | - Sheng-Tong Dai
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Taiwan
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli County, Taiwan
| | - Chia-Man Chou
- Division of Pediatric Surgery, Department of Surgery, Taichung Veterans General Hospital, Taichung City, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung City, Taiwan
| | - Sheng-Yang Huang
- Division of Pediatric Surgery, Department of Surgery, Taichung Veterans General Hospital, Taichung City, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung City, Taiwan
- * E-mail:
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Lee S, Kang M, Byeon K, Lee SE, Lee IH, Kim YA, Kang SW, Park JT. Machine Learning-Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features. J Digit Imaging 2022; 35:1091-1100. [PMID: 35411524 PMCID: PMC9582094 DOI: 10.1007/s10278-022-00625-8] [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: 03/16/2021] [Revised: 03/24/2022] [Accepted: 03/26/2022] [Indexed: 11/27/2022] Open
Abstract
Although ultrasound plays an important role in the diagnosis of chronic kidney disease (CKD), image interpretation requires extensive training. High operator variability and limited image quality control of ultrasound images have made the application of computer-aided diagnosis (CAD) challenging. This study assessed the effect of integrating computer-extracted measurable features with the convolutional neural network (CNN) on the ultrasound image CAD accuracy of CKD. Ultrasound images from patients who visited Severance Hospital and Gangnam Severance Hospital in South Korea between 2011 and 2018 were used. A Mask regional CNN model was used for organ segmentation and measurable feature extraction. Data on kidney length and kidney-to-liver echogenicity ratio were extracted. The ResNet18 model classified kidney ultrasound images into CKD and non-CKD. Experiments were conducted with and without the input of the measurable feature data. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). A total of 909 patients (mean age, 51.4 ± 19.3 years; 414 [49.5%] men and 495 [54.5%] women) were included in the study. The average AUROC from the model trained using ultrasound images achieved a level of 0.81. Image training with the integration of automatically extracted kidney length and echogenicity features revealed an improved average AUROC of 0.88. This value further increased to 0.91 when the clinical information of underlying diabetes was also included in the model trained with CNN and measurable features. The automated step-wise machine learning-aided model segmented, measured, and classified the kidney ultrasound images with high performance. The integration of computer-extracted measurable features into the machine learning model may improve CKD classification.
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Affiliation(s)
- Sangmi Lee
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Korea
| | | | | | - Sang Eun Lee
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Biostatics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - In Ho Lee
- AI Team, INFINYX, Daegu, Republic of Korea
| | - Young Ah Kim
- Department of Medical Informatics, Yonsei University Health System, Seoul, Korea
| | - Shin-Wook Kang
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Korea
| | - Jung Tak Park
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Korea.
- Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
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Khanna NN, Maindarkar M, Puvvula A, Paul S, Bhagawati M, Ahluwalia P, Ruzsa Z, Sharma A, Munjral S, Kolluri R, Krishnan PR, Singh IM, Laird JR, Fatemi M, Alizad A, Dhanjil SK, Saba L, Balestrieri A, Faa G, Paraskevas KI, Misra DP, Agarwal V, Sharma A, Teji J, Al-Maini M, Nicolaides A, Rathore V, Naidu S, Liblik K, Johri AM, Turk M, Sobel DW, Pareek G, Miner M, Viskovic K, Tsoulfas G, Protogerou AD, Mavrogeni S, Kitas GD, Fouda MM, Kalra MK, Suri JS. Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report. J Cardiovasc Dev Dis 2022; 9:jcdd9080268. [PMID: 36005433 PMCID: PMC9409845 DOI: 10.3390/jcdd9080268] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 07/30/2022] [Accepted: 08/09/2022] [Indexed: 12/15/2022] Open
Abstract
The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.
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Affiliation(s)
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Mahesh Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Anudeep Puvvula
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Annu’s Hospitals for Skin and Diabetes, Nellore 524101, India
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India
| | - Zoltan Ruzsa
- Invasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Smiksha Munjral
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Raghu Kolluri
- Ohio Health Heart and Vascular, Columbus, OH 43214, USA
| | | | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Surinder K. Dhanjil
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
| | - Antonella Balestrieri
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria, 09124 Cagliari, Italy
| | | | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Aman Sharma
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Jagjit Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, 2408 Nicosia, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA 95119, USA
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
| | - Kiera Liblik
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany
| | - David W. Sobel
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA
| | - Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 17674 Athens, Greece
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Manudeep K. Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Correspondence: ; Tel.: +1-916-749-5628
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Chen J, Jin P, Song Y, Feng L, Lu J, Chen H, Xin L, Qiu F, Cong Z, Shen J, Zhao Y, Xu W, Cai C, Zhou Y, Yang J, Zhang C, Chen Q, Jing X, Huang P. Auto-Segmentation Ultrasound-Based Radiomics Technology to Stratify Patient With Diabetic Kidney Disease: A Multi-Center Retrospective Study. Front Oncol 2022; 12:876967. [PMID: 35860551 PMCID: PMC9290767 DOI: 10.3389/fonc.2022.876967] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/31/2022] [Indexed: 12/12/2022] Open
Abstract
Background An increasing proportion of patients with diabetic kidney disease (DKD) has been observed among incident hemodialysis patients in large cities, which is consistent with the continuous growth of diabetes in the past 20 years. Purpose In this multicenter retrospective study, we developed a deep learning (DL)-based automatic segmentation and radiomics technology to stratify patients with DKD and evaluate the possibility of clinical application across centers. Materials and Methods The research participants were enrolled retrospectively and separated into three parts: training, validation, and independent test datasets for further analysis. DeepLabV3+ network, PyRadiomics package, and least absolute shrinkage and selection operator were used for segmentation, extraction of radiomics variables, and regression, respectively. Results A total of 499 patients from three centers were enrolled in this study including 246 patients with type II diabetes mellitus (T2DM) and 253 patients with DKD. The mean intersection-over-union (Miou) and mean pixel accuracy (mPA) of automatic segmentation of the data from the three medical centers were 0.812 ± 0.003, 0.781 ± 0.009, 0.805 ± 0.020 and 0.890 ± 0.004, 0.870 ± 0.002, 0.893 ± 0.007, respectively. The variables from the renal parenchyma and sinus provided different information for the diagnosis and follow-up of DKD. The area under the curve (AUC) of the radiomics model for differentiating between DKD and T2DM patients was 0.674 ± 0.074 and for differentiating between the high and low stages of DKD was 0.803 ± 0.037. Conclusion In this study, we developed a DL-based automatic segmentation, radiomics technology to stratify patients with DKD. The DL technology was proposed to achieve fast and accurate anatomical-level segmentation in the kidney, and an ultrasound-based radiomics model can achieve high diagnostic performance in the diagnosis and follow-up of patients with DKD.
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Affiliation(s)
- Jifan Chen
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Peile Jin
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Yue Song
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Liting Feng
- Department of Ultrasound, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiayue Lu
- Department of Clinical Laboratory, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hongjian Chen
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Post-Doctoral Research Center, Hangzhou Supor South Ocean Pharmaceutical Co., Ltd, Hangzhou, China
| | - Lei Xin
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Fuqiang Qiu
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Zhang Cong
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Jiaxin Shen
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Yanan Zhao
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Wen Xu
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Chenxi Cai
- Department of Ultrasound, The People’s Hospital of Yinshang, Anhui, China
| | - Yan Zhou
- Department of Ultrasound, Tianjin Third Central Hospital, Tianjin, China
| | - Jinfeng Yang
- Department of Ultrasound, The People’s Hospital of Yinshang, Anhui, China
| | - Chao Zhang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Qin Chen
- Department of Ultrasound, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Pintong Huang, ; Xiang Jing, ; Qin Chen,
| | - Xiang Jing
- Department of Ultrasound, Tianjin Third Central Hospital, Tianjin, China
- *Correspondence: Pintong Huang, ; Xiang Jing, ; Qin Chen,
| | - Pintong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, China
- *Correspondence: Pintong Huang, ; Xiang Jing, ; Qin Chen,
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Deep learning-based quality-controlled spleen assessment from ultrasound images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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36
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Betzler BK, Rim TH, Sabanayagam C, Cheng CY. Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging. Front Digit Health 2022; 4:889445. [PMID: 35706971 PMCID: PMC9190759 DOI: 10.3389/fdgth.2022.889445] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/06/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial Intelligence (AI) analytics has been used to predict, classify, and aid clinical management of multiple eye diseases. Its robust performances have prompted researchers to expand the use of AI into predicting systemic, non-ocular diseases and parameters based on ocular images. Herein, we discuss the reasons why the eye is well-suited for systemic applications, and review the applications of deep learning on ophthalmic images in the prediction of demographic parameters, body composition factors, and diseases of the cardiovascular, hematological, neurodegenerative, metabolic, renal, and hepatobiliary systems. Three main imaging modalities are included—retinal fundus photographs, optical coherence tomographs and external ophthalmic images. We examine the range of systemic factors studied from ophthalmic imaging in current literature and discuss areas of future research, while acknowledging current limitations of AI systems based on ophthalmic images.
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Affiliation(s)
- Bjorn Kaijun Betzler
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Tyler Hyungtaek Rim
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Charumathi Sabanayagam
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
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Song SH, Han JH, Kim KS, Cho YA, Youn HJ, Kim YI, Kweon J. Deep-learning segmentation of ultrasound images for automated calculation of the hydronephrosis area to renal parenchyma ratio. Investig Clin Urol 2022; 63:455-463. [PMID: 35670007 PMCID: PMC9262488 DOI: 10.4111/icu.20220085] [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: 03/01/2022] [Revised: 04/14/2022] [Accepted: 04/26/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose We investigated the feasibility of measuring the hydronephrosis area to renal parenchyma (HARP) ratio from ultrasound images using a deep-learning network. Materials and Methods The coronal renal ultrasound images of 195 pediatric and adolescent patients who underwent pyeloplasty to repair ureteropelvic junction obstruction were retrospectively reviewed. After excluding cases without a representative longitudinal renal image, we used a dataset of 168 images for deep-learning segmentation. Ten novel networks, such as combinations of DeepLabV3+ and UNet++, were assessed for their ability to calculate hydronephrosis and kidney areas, and the ensemble method was applied for further improvement. By dividing the image set into four, cross-validation was conducted, and the segmentation performance of the deep-learning network was evaluated using sensitivity, specificity, and dice similarity coefficients by comparison with the manually traced area. Results All 10 networks and ensemble methods showed good visual correlation with the manually traced kidney and hydronephrosis areas. The dice similarity coefficient of the 10-model ensemble was 0.9108 on average, and the best 5-model ensemble had a dice similarity coefficient of 0.9113 on average. We included patients with severe hydronephrosis who underwent renal ultrasonography at a single institution; thus, external validation of our algorithm in a heterogeneous ultrasonography examination setup with a diverse set of instruments is recommended. Conclusions Deep-learning-based calculation of the HARP ratio is feasible and showed high accuracy for imaging of the severity of hydronephrosis using ultrasonography. This algorithm can help physicians make more accurate and reproducible diagnoses of hydronephrosis using ultrasonography.
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Affiliation(s)
- Sang Hoon Song
- Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jae Hyeon Han
- Department of Urology, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Korea
| | - Kun Suk Kim
- Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young Ah Cho
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hye Jung Youn
- Department of Convergence Medicine, Asan Medical Center, Seoul, Korea
| | - Young In Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Korea
| | - Jihoon Kweon
- Department of Convergence Medicine, Asan Medical Center, Seoul, Korea.
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Alex DM, Chandy DA, Christinal AH, Singh A, Pushkaran M. A Hybrid Random Forest Classifier for Chronic Kidney Disease Prediction from 2D Ultrasound Kidney Images. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422560109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Chronic kidney disease (CKD) is one of the causes of mortality in almost all countries across the globe and the notable thing is its asymptomatic nature in the early stages. This disease is characterized by the gradual loss of kidney function in an individual. Frequently chronic kidney disease is diagnosed based on the Estimated Glomerular Filtration Rate (eGFR) determined from blood and urine tests. In order to reduce the risk factors arising due to chronic kidney disease, it is essential to be diagnosed in the earlier stages itself. This work proposes an automated chronic kidney disease detection based on the textural features of the kidney using a hybrid random forest classifier from 2D ultrasound kidney images. The proposed classifier is compared with the other competing machine learning classifiers through experimenting on a dataset of 150 images and gives a better accuracy of [Formula: see text] with [Formula: see text] of recall and precision, thus proving it to be promising in detecting CKD noninvasively in the early stages.
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Affiliation(s)
- Deepthy Mary Alex
- Department of ECE, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore - 641114, Tamil Nadu, India
| | - D. Abraham Chandy
- Department of ECE, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore - 641114, Tamil Nadu, India
| | - A. Hepzibah Christinal
- Department of Mathematics, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore - 641114, Tamil Nadu, India
| | - Arvinder Singh
- Department of Radiology, Sri Guru Ram Das Institute of Medical Sciences and Research, Sri Amritsar - 143501, Punjab, India
| | - M. Pushkaran
- Radiology Division, Kovai Diagnostic Centre, Coimbatore-641012, Tamil Nadu, India
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Jagtap JM, Gregory AV, Homes HL, Wright DE, Edwards ME, Akkus Z, Erickson BJ, Kline TL. Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements. Abdom Radiol (NY) 2022; 47:2408-2419. [PMID: 35476147 PMCID: PMC9226108 DOI: 10.1007/s00261-022-03521-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 11/01/2022]
Abstract
PURPOSE Total kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert annotators. We investigated a deep learning-based approach for automated segmentation of TKV from 3D US in ADPKD patients. METHOD We used axially acquired 3D US-kidney images in 22 ADPKD patients where each patient and each kidney were scanned three times, resulting in 132 scans that were manually segmented. We trained a convolutional neural network to segment the whole kidney and measure TKV. All patients were subsequently imaged with MRI for measurement comparison. RESULTS Our method automatically segmented polycystic kidneys in 3D US images obtaining an average Dice coefficient of 0.80 on the test dataset. The kidney volume measurement compared with linear regression coefficient and bias from human tracing were R2 = 0.81, and - 4.42%, and between AI and reference standard were R2 = 0.93, and - 4.12%, respectively. MRI and US measured kidney volumes had R2 = 0.84 and a bias of 7.47%. CONCLUSION This is the first study applying deep learning to 3D US in ADPKD. Our method shows promising performance for auto-segmentation of kidneys using 3D US to measure TKV, close to human tracing and MRI measurement. This imaging and analysis method may be useful in a number of settings, including pediatric imaging, clinical studies, and longitudinal tracking of patient disease progression.
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40
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Artificial intelligence-enabled decision support in nephrology. Nat Rev Nephrol 2022; 18:452-465. [PMID: 35459850 DOI: 10.1038/s41581-022-00562-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2022] [Indexed: 12/12/2022]
Abstract
Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and treatment. Emerging evidence suggests that artificial intelligence (AI)-enabled decision support systems - which use algorithms based on learned examples - may have an important role in nephrology. Contemporary AI applications can accurately predict the onset of acute kidney injury before notable biochemical changes occur; can identify modifiable risk factors for chronic kidney disease onset and progression; can match or exceed human accuracy in recognizing renal tumours on imaging studies; and may augment prognostication and decision-making following renal transplantation. Future AI applications have the potential to make real-time, continuous recommendations for discrete actions and yield the greatest probability of achieving optimal kidney health outcomes. Realizing the clinical integration of AI applications will require cooperative, multidisciplinary commitment to ensure algorithm fairness, overcome barriers to clinical implementation, and build an AI-competent workforce. AI-enabled decision support should preserve the pre-eminence of wisdom and augment rather than replace human decision-making. By anchoring intuition with objective predictions and classifications, this approach should favour clinician intuition when it is honed by experience.
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41
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Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022; 11:jcm11082265. [PMID: 35456357 PMCID: PMC9031863 DOI: 10.3390/jcm11082265] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.
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Affiliation(s)
- Ștefan Busnatu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
| | - Alexandra Bolocan
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - George E. D. Petrescu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Dan Nicolae Păduraru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Iulian Năstasă
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Mircea Lupușoru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Marius Geantă
- Centre for Innovation in Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Octavian Andronic
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 50044 Bucharest, Romania
- Correspondence:
| | - Henrique Martins
- Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilha, Portugal;
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Oomen L, Bootsma-Robroeks C, Cornelissen E, de Wall L, Feitz W. Pearls and Pitfalls in Pediatric Kidney Transplantation After 5 Decades. Front Pediatr 2022; 10:856630. [PMID: 35463874 PMCID: PMC9024248 DOI: 10.3389/fped.2022.856630] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/15/2022] [Indexed: 11/13/2022] Open
Abstract
Worldwide, over 1,300 pediatric kidney transplantations are performed every year. Since the first transplantation in 1959, healthcare has evolved dramatically. Pre-emptive transplantations with grafts from living donors have become more common. Despite a subsequent improvement in graft survival, there are still challenges to face. This study attempts to summarize how our understanding of pediatric kidney transplantation has developed and improved since its beginnings, whilst also highlighting those areas where future research should concentrate in order to help resolve as yet unanswered questions. Existing literature was compared to our own data of 411 single-center pediatric kidney transplantations between 1968 and 2020, in order to find discrepancies and allow identification of future challenges. Important issues for future care are innovations in immunosuppressive medication, improving medication adherence, careful donor selection with regard to characteristics of both donor and recipient, improvement of surgical techniques and increased attention for lower urinary tract dysfunction and voiding behavior in all patients.
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Affiliation(s)
- Loes Oomen
- Division of Pediatric Urology, Department of Urology, Radboudumc Amalia Children's Hospital, Nijmegen, Netherlands
| | - Charlotte Bootsma-Robroeks
- Department of Pediatric Nephrology, Radboudumc Amalia Children's Hospital, Nijmegen, Netherlands
- Department of Pediatrics, Pediatric Nephrology, Beatrix Children's Hospital, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Elisabeth Cornelissen
- Department of Pediatric Nephrology, Radboudumc Amalia Children's Hospital, Nijmegen, Netherlands
| | - Liesbeth de Wall
- Division of Pediatric Urology, Department of Urology, Radboudumc Amalia Children's Hospital, Nijmegen, Netherlands
| | - Wout Feitz
- Division of Pediatric Urology, Department of Urology, Radboudumc Amalia Children's Hospital, Nijmegen, Netherlands
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A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5976155. [PMID: 35345803 PMCID: PMC8957411 DOI: 10.1155/2022/5976155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/18/2022] [Accepted: 02/22/2022] [Indexed: 11/17/2022]
Abstract
The paper proposes an A-ResNet model to improve ResNet. The residual attention module with shortcut connection is introduced to enhance the focus on the target object; the dropout layer is introduced to prevent the overfitting phenomenon and improve the recognition accuracy; the network architecture is adjusted to accelerate the training convergence speed and improve the recognition accuracy. The experimental results show that the A-ResNet model achieves a top-1 accuracy improvement of about 2% compared with the traditional ResNet network. Image recognition is one of the core technologies of computer vision, but its application in the field of tea is relatively small, and tea recognition still relies on sensory review methods. A total of 1,713 images of eight common green teas were collected, and the modeling effects of different network depths and different optimization algorithms were explored from the perspectives of predictive ability, convergence speed, model size, and recognition equilibrium of recognition models.
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44
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Zhou XJ, Zhong XH, Duan LX. Integration of Artificial Intelligence And Multi-omics in Kidney Diseases. FUNDAMENTAL RESEARCH 2022. [DOI: 10.1016/j.fmre.2022.01.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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45
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Ishikawa M, Inoue T, Kozawa E, Okada H, Kobayashi N. Framework for estimating renal function using magnetic resonance imaging. J Med Imaging (Bellingham) 2022; 9:024501. [PMID: 35360418 PMCID: PMC8923691 DOI: 10.1117/1.jmi.9.2.024501] [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: 07/01/2021] [Accepted: 02/23/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Nephrologists have empirically predicted renal function from renal morphology. In diagnosing a case of renal dysfunction of unknown course, acute kidney injury and chronic kidney disease are diagnosed from blood tests and an imaging study including magnetic resonance imaging (MRI), and an examination/treatment policy is determined. A framework for the estimation of renal function from water images obtained using the Dixon method is proposed to provide information that helps clinicians reach a diagnosis by accurately estimating renal function on the basis of renal MRI. Approach: The proposed framework consists of four steps. First, the kidney area is extracted by MRI using the Dixon method with a U-net by deep learning. Second, the extracted renal region is registered with the target mask. Third, the kidney features are calculated based on the target mask classification information created by a specialist. Fourth, the estimated glomerular filtration rate (eGFR) representing the renal function is estimated using a regression support vector machine from the calculated features. Results: For the accuracy evaluation, we conducted an experiment to estimate the eGFR when MRI was performed and the eGFR slope, which is the annual rate of decline in eGFR. When the accuracy was evaluated for 165 subjects, the eGFR was estimated to have a root mean square error (RMSE) of 11.99 and a correlation coefficient of 0.83. Moreover, the eGFR slope was estimated to have an RMSE of 4.8 and a correlation coefficient of 0.5. Conclusions: Therefore, the proposed method shows the possibility of estimating the prognosis of renal function based on water images obtained by the Dixon method.
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46
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Development and Validation of a Scoring System for Assessment of Clinical Failure after Pediatric Robot-Assisted Laparoscopic Extravesical Ureteral Reimplantation: A Multi-Center Study. J Clin Med 2022; 11:jcm11051327. [PMID: 35268417 PMCID: PMC8910908 DOI: 10.3390/jcm11051327] [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: 01/11/2022] [Revised: 02/17/2022] [Accepted: 02/25/2022] [Indexed: 02/04/2023] Open
Abstract
We aimed to develop and validate a scoring system as an objective assessment tool for predicting clinical failure after pediatric robotic extravesical ureteral reimplantation. Data for this multi-institutional retrospective cohort was obtained from two tertiary referral hospitals. We defined clinical failure as incomplete radiographic resolution or post-operative febrile UTI. Patients were stratified into low, intermediate, and high-risk groups according to the score. External validation was performed using the model projected to the external validation cohort. An amount of 115 renal units in the development cohort and 46 renal units in the validation cohort were analyzed. The prediction score was calculated with weighted points to each variable according to their regression coefficient as age (year) + BMI + BBD times 10 + VUR grade times 7 + console time (h) + hospital stay times 6. The C-index of our scoring system was 0.850 and 0.770 in the development and validation cohorts, respectively. Clinical failure was significantly different among risk groups: 0% (low-risk), 3.3% (intermediate-risk), and 22.2% (high-risk) (p = 0.004) in the development cohort. A novel scoring system using multiple pre- and intra-operative variables provides a prediction of children at risk of failure after robotic extravesical ureteral reimplantation.
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47
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Rabbi F, Dabbagh SR, Angin P, Yetisen AK, Tasoglu S. Deep Learning-Enabled Technologies for Bioimage Analysis. MICROMACHINES 2022; 13:mi13020260. [PMID: 35208385 PMCID: PMC8880650 DOI: 10.3390/mi13020260] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 01/31/2022] [Accepted: 02/03/2022] [Indexed: 02/05/2023]
Abstract
Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.
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Affiliation(s)
- Fazle Rabbi
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
| | - Sajjad Rahmani Dabbagh
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey
- Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkey
| | - Pelin Angin
- Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey;
| | - Ali Kemal Yetisen
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Savas Tasoglu
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey
- Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkey
- Institute of Biomedical Engineering, Boğaziçi University, Çengelköy, Istanbul 34684, Turkey
- Physical Intelligence Department, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany
- Correspondence:
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48
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Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2793361. [PMID: 35154618 PMCID: PMC8831050 DOI: 10.1155/2022/2793361] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 01/13/2022] [Accepted: 01/15/2022] [Indexed: 01/12/2023]
Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.
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49
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Osipova EV, Osipova EA, Melnikova LV. Significance of modern diagnostic methods in the early diagnosis of hypertensive nephropathy. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2022. [DOI: 10.15829/1728-8800-2022-2882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
The review analyzes modern criteria for early diagnosis of renal damage in hypertensive patients. Based on numerous studies, the role of impaired renal function markers and renal artery Doppler ultrasound in the diagnosis of hypertensive nephropathy is indicated. A search was made for the relationship between laboratory criteria for kidney damage and renal Doppler ultrasound characteristics in hypertensive patients. In addition, the importance of revealing intrarenal hemodynamic disorders in the early diagnosis of hypertensive nephropathy was established.
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Affiliation(s)
- E. V. Osipova
- Penza Institute for Postgraduate Medical Education — branch of the Russian Medical Academy of Continuous Professional Education
| | | | - L. V. Melnikova
- Russian Medical Academy of Continuous Professional Education
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50
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Zhao L, Lediju Bell MA. A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients. BME FRONTIERS 2022; 2022:9780173. [PMID: 36714302 PMCID: PMC9880989 DOI: 10.34133/2022/9780173] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
The massive and continuous spread of COVID-19 has motivated researchers around the world to intensely explore, understand, and develop new techniques for diagnosis and treatment. Although lung ultrasound imaging is a less established approach when compared to other medical imaging modalities such as X-ray and CT, multiple studies have demonstrated its promise to diagnose COVID-19 patients. At the same time, many deep learning models have been built to improve the diagnostic efficiency of medical imaging. The integration of these initially parallel efforts has led multiple researchers to report deep learning applications in medical imaging of COVID-19 patients, most of which demonstrate the outstanding potential of deep learning to aid in the diagnosis of COVID-19. This invited review is focused on deep learning applications in lung ultrasound imaging of COVID-19 and provides a comprehensive overview of ultrasound systems utilized for data acquisition, associated datasets, deep learning models, and comparative performance.
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Affiliation(s)
- Lingyi Zhao
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Muyinatu A. Lediju Bell
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA,Department of Computer Science, Johns Hopkins University, Baltimore, USA,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, USA
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