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Jang DH, Lee J, Jeon YJ, Yoon YE, Ahn H, Kang BK, Choi WS, Oh J, Lee DK. Kidney, ureter, and urinary bladder segmentation based on non-contrast enhanced computed tomography images using modified U-Net. Sci Rep 2024; 14:15325. [PMID: 38961140 PMCID: PMC11222420 DOI: 10.1038/s41598-024-66045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 06/26/2024] [Indexed: 07/05/2024] Open
Abstract
This study was performed to segment the urinary system as the basis for diagnosing urinary system diseases on non-contrast computed tomography (CT). This study was conducted with images obtained between January 2016 and December 2020. During the study period, non-contrast abdominopelvic CT scans of patients and diagnosed and treated with urinary stones at the emergency departments of two institutions were collected. Region of interest extraction was first performed, and urinary system segmentation was performed using a modified U-Net. Thereafter, fivefold cross-validation was performed to evaluate the robustness of the model performance. In fivefold cross-validation results of the segmentation of the urinary system, the average dice coefficient was 0.8673, and the dice coefficients for each class (kidney, ureter, and urinary bladder) were 0.9651, 0.7172, and 0.9196, respectively. In the test dataset, the average dice coefficient of best performing model in fivefold cross validation for whole urinary system was 0.8623, and the dice coefficients for each class (kidney, ureter, and urinary bladder) were 0.9613, 0.7225, and 0.9032, respectively. The segmentation of the urinary system using the modified U-Net proposed in this study could be the basis for the detection of kidney, ureter, and urinary bladder lesions, such as stones and tumours, through machine learning.
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Affiliation(s)
- Dong-Hyun Jang
- Department of Public Healthcare Service, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Juncheol Lee
- Department of Emergency Medicine, College of Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | | | - Young Eun Yoon
- Department of Urology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Hyungwoo Ahn
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Bo-Kyeong Kang
- Department of Radiology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Won Seok Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jaehoon Oh
- Department of Emergency Medicine, College of Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
| | - Dong Keon Lee
- Department of Emergency Medicine, Seoul National University Bundang Hospital, 13620, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea.
- Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
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2
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Xing Z, Zhu Z, Jiang Z, Zhao J, Chen Q, Xing W, Pan L, Zeng Y, Liu A, Ding J. Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:444-454. [PMID: 38343222 PMCID: PMC11031534 DOI: 10.1007/s10278-023-00946-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/18/2023] [Accepted: 10/18/2023] [Indexed: 04/20/2024]
Abstract
To develop a fully automatic urinary stone detection system (kidney, ureter, and bladder) and to test it in a real clinical environment. The local institutional review board approved this retrospective single-center study that used non-enhanced abdominopelvic CT scans from patients admitted urology (uPatients) and emergency (ePatients). The uPatients were randomly divided into training and validation sets in a ratio of 3:1. We designed a cascade urinary stone map location-feature pyramid networks (USm-FPNs) and innovatively proposed a ureter distance heatmap method to estimate the ureter position on non-enhanced CT to further reduce the false positives. The performances of the system were compared using the free-response receiver operating characteristic curve and the precision-recall curve. This study included 811 uPatients and 356 ePatients. At stone level, the cascade detector USm-FPNs has the mean of false positives per scan (mFP) 1.88 with the sensitivity 0.977 in validation set, and mFP was further reduced to 1.18 with the sensitivity 0.977 after combining the ureter distance heatmap. At patient level, the sensitivity and precision were as high as 0.995 and 0.990 in validation set, respectively. In a real clinical set of ePatients (27.5% of patients contain stones), the mFP was 1.31 with as high as sensitivity 0.977, and the diagnostic time reduced by > 20% with the system help. A fully automatic detection system for entire urinary stones on non-enhanced CT scans was proposed and reduces obviously the burden on junior radiologists without compromising sensitivity in real emergency data.
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Affiliation(s)
- Zhaoyu Xing
- Department of Urology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Zuhui Zhu
- Department of Radiology, Nantong Hospital of Traditional Chinese Medicine, Nantong, Jiangsu, China
| | - Zhenxing Jiang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Jingshi Zhao
- Department of Radiology, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qin Chen
- Department of Radiology, People's Hospital of Pengzhou, Chengdu, Sichuan, China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Liang Pan
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Yan Zeng
- Department of Research Center, Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
| | - Aie Liu
- Department of Research Center, Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
| | - Jiule Ding
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
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3
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Ahmed F, Abbas S, Athar A, Shahzad T, Khan WA, Alharbi M, Khan MA, Ahmed A. Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence. Sci Rep 2024; 14:6173. [PMID: 38486010 PMCID: PMC10940612 DOI: 10.1038/s41598-024-56478-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.
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Affiliation(s)
- Fahad Ahmed
- School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
| | - Sagheer Abbas
- Department of Computer Sciences, Bahria University, Lahore Campus, Lahore, 54000, Pakistan
| | - Atifa Athar
- Department of Computer Science, Comsats University Islamabad, Lahore Campus, Lahore, 54000, Pakistan
| | - Tariq Shahzad
- Department of Computer Sciences, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan
| | - Wasim Ahmad Khan
- School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
| | - Meshal Alharbi
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, 11942, Alkharj, Saudi Arabia
| | - Muhammad Adnan Khan
- School of Computing, Skyline University College, University City Sharjah, 1797, Sharjah, UAE.
- Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13120, Republic of Korea.
- Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan.
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
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Yang K, Song J, Liu M, Xue L, Liu S, Yin X, Liu K. TBACkp: HER2 expression status classification network focusing on intrinsic subenvironmental characteristics of breast cancer liver metastases. Comput Biol Med 2024; 170:108002. [PMID: 38277921 DOI: 10.1016/j.compbiomed.2024.108002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/24/2023] [Accepted: 01/13/2024] [Indexed: 01/28/2024]
Abstract
The HER2 expression status in breast cancer liver metastases is a crucial indicator for the diagnosis, treatment, and prognosis assessment of patients. And typical diagnosis involves assessing the HER2 expression status through invasive procedures like biopsy. However, this method has certain drawbacks, such as being difficult in obtaining tissue samples and requiring long examination periods. To address these limitations, we propose an AI-aided diagnostic model. This model enables rapid diagnosis. It diagnoses a patient's HER2 expression status on the basis of preprocessed images, which is the region of the lesion extracted from a CT image rather than from an actual tissue sample. The algorithm of the model adopts a parallel structure, including a Branch Block and a Trunk Block. The Branch Block is responsible for extracting the gradient characteristics between the tumor sub-environments, and the Trunk Block is for fusing the characteristics extracted by the Branch Block. The Branch Block contains CNN with self-attention, which combines the advantages of CNN and self-attention to extract more meticulous and comprehensive image features. And the Trunk Block is so designed that it fuses the extracted image feature information without affecting the transmission of the original image features. The Conv-Attention is used to calculate the attention in the Trunk Block, which uses kernel dot product and is responsible for providing the weight for the self-attention in the process of using convolution induced deviation calculation. Combined with the structure of the model and the method used, we refer to this model as TBACkp. The dataset comprises the enhanced abdominal CT images of 151 patients with liver metastases from breast cancer, together with the corresponding HER2 expression levels for each patient. The experimental results are as follows: (AUC: 0.915, ACC: 0.854, specificity: 0.809, precision: 0.863, recall: 0.881, F1-score: 0.872). The results demonstrate that this method can accurately assess the HER2 expression status in patients when compared with other advanced deep learning model.
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Affiliation(s)
- Kun Yang
- College of Quality and Technical Supervision, Hebei University, Baoding, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding, China; Scientific Research and Innovation Team of Hebei University, Baoding, China
| | - Jie Song
- College of Quality and Technical Supervision, Hebei University, Baoding, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding, China; Scientific Research and Innovation Team of Hebei University, Baoding, China
| | - Meng Liu
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
| | - Linyan Xue
- College of Quality and Technical Supervision, Hebei University, Baoding, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding, China; Scientific Research and Innovation Team of Hebei University, Baoding, China
| | - Shuang Liu
- College of Quality and Technical Supervision, Hebei University, Baoding, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding, China; Scientific Research and Innovation Team of Hebei University, Baoding, China
| | - Xiaoping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China; Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Hebei University, Baoding, China; The Outstanding Young Scientific Research and Innovation Team of Hebei University, Baoding, China.
| | - Kun Liu
- College of Quality and Technical Supervision, Hebei University, Baoding, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding, China; Scientific Research and Innovation Team of Hebei University, Baoding, China.
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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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Tano ZE, Cumpanas AD, Gorgen ARH, Rojhani A, Altamirano-Villarroel J, Landman J. Surgical Artificial Intelligence: Endourology. Urol Clin North Am 2024; 51:77-89. [PMID: 37945104 DOI: 10.1016/j.ucl.2023.06.004] [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] [Indexed: 11/12/2023]
Abstract
Endourology is ripe with information that includes patient factors, laboratory tests, outcomes, and visual data, which is becoming increasingly complex to assess. Artificial intelligence (AI) has the potential to explore and define these relationships; however, humans might not be involved in the input, analysis, or even determining the methods of analysis. Herein, the authors present the current state of AI in endourology and highlight the need for urologists to share their proposed AI solutions for reproducibility outside of their institutions and prepare themselves to properly critique this new technology.
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Affiliation(s)
- Zachary E Tano
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA.
| | - Andrei D Cumpanas
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Antonio R H Gorgen
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Allen Rojhani
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Jaime Altamirano-Villarroel
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Jaime Landman
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
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Asif S, Zhao M, Chen X, Zhu Y. StoneNet: An Efficient Lightweight Model Based on Depthwise Separable Convolutions for Kidney Stone Detection from CT Images. Interdiscip Sci 2023; 15:633-652. [PMID: 37452930 DOI: 10.1007/s12539-023-00578-8] [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: 03/15/2023] [Revised: 06/27/2023] [Accepted: 06/30/2023] [Indexed: 07/18/2023]
Abstract
Kidney stone disease is one of the most common and serious health problems in much of the world, leading to many hospitalizations with severe pain. Detecting small stones is difficult and time-consuming, so an early diagnosis of kidney disease is needed to prevent the loss of kidney failure. Recent advances in artificial intelligence (AI) found to be very successful in the diagnosis of various diseases in the biomedical field. However, existing models using deep networks have several problems, such as high computational cost, long training time, and huge parameters. Providing a low-cost solution for diagnosing kidney stones in a medical decision support system is of paramount importance. Therefore, in this study, we propose "StoneNet", a lightweight and high-performance model for the detection of kidney stones based on MobileNet using depthwise separable convolution. The proposed model includes a combination of global average pooling (GAP), batch normalization, dropout layer, and dense layers. Our study shows that using GAP instead of flattening layers greatly improves the robustness of the model by significantly reducing the parameters. The developed model is benchmarked against four pre-trained models as well as the state-of-the-art heavy model. The results show that the proposed model can achieve the highest accuracy of 97.98%, and only requires training and testing time of 996.88 s and 14.62 s. Several parameters, such as different batch sizes and optimizers, were considered to validate the proposed model. The proposed model is computationally faster and provides optimal performance than other considered models. Experiments on a large kidney dataset of 1799 CT images show that StoneNet has superior performance in terms of higher accuracy and lower complexity. The proposed model can assist the radiologist in faster diagnosis of kidney stones and has great potential for deployment in real-time applications.
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Affiliation(s)
- Sohaib Asif
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Ming Zhao
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Xuehan Chen
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Yusen Zhu
- School of Mathematics, Hunan University, Changsha, China
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Ji Y, Hwang G, Lee SJ, Lee K, Yoon H. A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs. Front Vet Sci 2023; 10:1236579. [PMID: 37799401 PMCID: PMC10548669 DOI: 10.3389/fvets.2023.1236579] [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: 06/07/2023] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Nephrolithiasis is one of the most common urinary disorders in dogs. Although a majority of kidney calculi are non-obstructive and are likely to be asymptomatic, they can lead to parenchymal loss and obstruction as they progress. Thus, early diagnosis of kidney calculi is important for patient monitoring and better prognosis. However, detecting kidney calculi and monitoring changes in the sizes of the calculi from computed tomography (CT) images is time-consuming for clinicians. This study, in a first of its kind, aims to develop a deep learning model for automatic kidney calculi detection using pre-contrast CT images of dogs. A total of 34,655 transverseimage slices obtained from 76 dogs with kidney calculi were used to develop the deep learning model. Because of the differences in kidney location and calculi sizes in dogs compared to humans, several processing methods were used. The first stage of the models, based on the Attention U-Net (AttUNet), was designed to detect the kidney for the coarse feature map. Five different models-AttUNet, UTNet, TransUNet, SwinUNet, and RBCANet-were used in the second stage to detect the calculi in the kidneys, and the performance of the models was evaluated. Compared with a previously developed model, all the models developed in this study yielded better dice similarity coefficients (DSCs) for the automatic segmentation of the kidney. To detect kidney calculi, RBCANet and SwinUNet yielded the best DSC, which was 0.74. In conclusion, the deep learning model developed in this study can be useful for the automated detection of kidney calculi.
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Affiliation(s)
- Yewon Ji
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
| | - Gyeongyeon Hwang
- Division of Electronic Engineering, College of Engineering, Jeonbuk National University, Jeonju, Republic of Korea
| | - Sang Jun Lee
- Division of Electronic Engineering, College of Engineering, Jeonbuk National University, Jeonju, Republic of Korea
| | - Kichang Lee
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
| | - Hakyoung Yoon
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
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Shim JH, Choi SY, Chang IH, Park SB. Dose Optimization Using a Deep Learning Tool in Various CT Protocols for Urolithiasis: A Physical Human Phantom Study. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1677. [PMID: 37763796 PMCID: PMC10538199 DOI: 10.3390/medicina59091677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 09/12/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023]
Abstract
Background and Objectives: We attempted to determine the optimal radiation dose to maintain image quality using a deep learning application in a physical human phantom. Materials and Methods: Three 5 × 5 × 5 mm3 uric acid stones were placed in a physical human phantom in various locations. Three tube voltages (120, 100, and 80 kV) and four current-time products (100, 70, 30, and 15 mAs) were implemented in 12 scans. Each scan was reconstructed with filtered back projection (FBP), statistical iterative reconstruction (IR, iDose), and knowledge-based iterative model reconstruction (IMR). By applying deep learning to each image, we took 12 more scans. Objective image assessments were calculated using the standard deviation of the Hounsfield unit (HU). Subjective image assessments were performed by one radiologist and one urologist. Two radiologists assessed the subjective assessment and found the stone under the absence of information. We used this data to calculate the diagnostic accuracy. Results: Objective image noise was decreased after applying a deep learning tool in all images of FBP, iDose, and IMR. There was no statistical difference between iDose and deep learning-applied FBP images (10.1 ± 11.9, 9.5 ± 18.5 HU, p = 0.583, respectively). At a 100 kV-30 mAs setting, deep learning-applied FBP obtained a similar objective noise in approximately one third of the radiation doses compared to FBP. In radiation doses with settings lower than 100 kV-30 mAs, the subject image assessment (image quality, confidence level, and noise) showed deteriorated scores. Diagnostic accuracy was increased when the deep learning setting was lower than 100 kV-30 mAs, except for at 80 kV-15 mAs. Conclusions: At the setting of 100 kV-30 mAs or higher, deep learning-applied FBP did not differ in image quality compared to IR. At the setting of 100 kV-30 mAs, the radiation dose can decrease by about one third while maintaining objective noise.
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Affiliation(s)
- Jae Hun Shim
- Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea
| | - Se Young Choi
- Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea
| | - In Ho Chang
- Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea
| | - Sung Bin Park
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea
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Jiang X, Hu Z, Wang S, Zhang Y. Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers (Basel) 2023; 15:3608. [PMID: 37509272 PMCID: PMC10377683 DOI: 10.3390/cancers15143608] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/10/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.
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Grants
- RM32G0178B8 BBSRC
- MC_PC_17171 MRC, UK
- RP202G0230 Royal Society, UK
- AA/18/3/34220 BHF, UK
- RM60G0680 Hope Foundation for Cancer Research, UK
- P202PF11 GCRF, UK
- RP202G0289 Sino-UK Industrial Fund, UK
- P202ED10, P202RE969 LIAS, UK
- P202RE237 Data Science Enhancement Fund, UK
- 24NN201 Fight for Sight, UK
- OP202006 Sino-UK Education Fund, UK
- RM32G0178B8 BBSRC, UK
- 2023SJZD125 Major project of philosophy and social science research in colleges and universities in Jiangsu Province, China
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Affiliation(s)
- Xiaoyan Jiang
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China; (X.J.); (Z.H.)
| | - Zuojin Hu
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China; (X.J.); (Z.H.)
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
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11
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Bhattacharjee A, Rabea S, Bhattacharjee A, Elkaeed EB, Murugan R, Selim HMRM, Sahu RK, Shazly GA, Salem Bekhit MM. A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images. Front Oncol 2023; 13:1193746. [PMID: 37333825 PMCID: PMC10272771 DOI: 10.3389/fonc.2023.1193746] [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: 03/27/2023] [Accepted: 05/04/2023] [Indexed: 06/20/2023] Open
Abstract
Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed tomography multi-class image classification. The proposed model obtained 99.39% accuracy, 99.33% precision, 98% recall, and 98.67% F1-score for lung cancer multi-class classification. Whereas, it attained 100% accuracy, F1 score, recall and precision for kidney disease multi-class classification. Also, the proposed modified Xception model outperformed the original Xception model and the existing methods. Hence, it can serve as a support tool to the radiologists and nephrologists for early detection of lung cancer and chronic kidney disease, respectively.
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Affiliation(s)
- Ananya Bhattacharjee
- Bio-Medical Imaging Laboratory (BIOMIL), Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
| | - Sameh Rabea
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
| | - Abhishek Bhattacharjee
- Department of Pharmaceutical Sciences, Assam University (A Central University), Silchar, India
| | - Eslam B. Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
| | - R. Murugan
- Bio-Medical Imaging Laboratory (BIOMIL), Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
| | - Heba Mohammed Refat M. Selim
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
- Microbiology and Immunology Department, Faculty of Pharmacy (Girls); Al-Azhar University, Cairo, Egypt
| | - Ram Kumar Sahu
- Department of Pharmaceutical Sciences, Hemvati Nandan Bahuguna Garhwal University (A Central University), Tehri Garhwal, India
| | - Gamal A. Shazly
- Kayyali Chair for Pharmaceutical Industry, Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mounir M. Salem Bekhit
- Kayyali Chair for Pharmaceutical Industry, Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
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12
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Gül Y, Yaman S, Avcı D, Çilengir AH, Balaban M, Güler H. A Novel Deep Transfer Learning-Based Approach for Automated Pes Planus Diagnosis Using X-ray Image. Diagnostics (Basel) 2023; 13:diagnostics13091662. [PMID: 37175053 PMCID: PMC10178173 DOI: 10.3390/diagnostics13091662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/02/2023] [Accepted: 05/06/2023] [Indexed: 05/15/2023] Open
Abstract
Pes planus, colloquially known as flatfoot, is a deformity defined as the collapse, flattening or loss of the medial longitudinal arch of the foot. The first standard radiographic examination for diagnosing pes planus involves lateral and dorsoplantar weight-bearing radiographs. Recently, many artificial intelligence-based computer-aided diagnosis (CAD) systems and models have been developed for the detection of various diseases from radiological images. However, to the best of our knowledge, no model and system has been proposed in the literature for automated pes planus diagnosis using X-ray images. This study presents a novel deep learning-based model for automated pes planus diagnosis using X-ray images, a first in the literature. To perform this study, a new pes planus dataset consisting of weight-bearing X-ray images was collected and labeled by specialist radiologists. In the preprocessing stage, the number of X-ray images was augmented and then divided into 4 and 16 patches, respectively in a pyramidal fashion. Thus, a total of 21 images are obtained for each image, including 20 patches and one original image. These 21 images were then fed to the pre-trained MobileNetV2 and 21,000 features were extracted from the Logits layer. Among the extracted deep features, the most important 1312 features were selected using the proposed iterative ReliefF algorithm, and then classified with support vector machine (SVM). The proposed deep learning-based framework achieved 95.14% accuracy using 10-fold cross validation. The results demonstrate that our transfer learning-based model can be used as an auxiliary tool for diagnosing pes planus in clinical practice.
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Affiliation(s)
- Yeliz Gül
- Department of Radiology, Elazig Fethi Sekin City Hospital, 23280 Elazig, Turkey
| | - Süleyman Yaman
- Biomedical Department, Vocational School of Technical Sciences, Firat University, 23119 Elazig, Turkey
| | - Derya Avcı
- Department of Software Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
| | - Atilla Hikmet Çilengir
- Department of Radiology, Faculty of Medicine, Izmir Democracy University, 35140 Izmir, Turkey
| | - Mehtap Balaban
- Department of Radiology, Faculty of Medicine, Ankara Yildirim Beyazit University, 06010 Ankara, Turkey
| | - Hasan Güler
- Electrical-Electronics Engineering Department, Engineering Faculty, Firat University, 23119 Elazig, Turkey
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13
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Badawy M, Almars AM, Balaha HM, Shehata M, Qaraad M, Elhosseini M. A two-stage renal disease classification based on transfer learning with hyperparameters optimization. Front Med (Lausanne) 2023; 10:1106717. [PMID: 37089598 PMCID: PMC10113505 DOI: 10.3389/fmed.2023.1106717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 03/14/2023] [Indexed: 04/09/2023] Open
Abstract
Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which affect anywhere from 1 to 15% of the global population and thus; considered one of the leading causes of chronic kidney diseases (CKD). In addition to kidney stones, renal cancer is the tenth most prevalent type of cancer, accounting for 2.5% of all cancers. Artificial intelligence (AI) in medical systems can assist radiologists and other healthcare professionals in diagnosing different renal diseases (RD) with high reliability. This study proposes an AI-based transfer learning framework to detect RD at an early stage. The framework presented on CT scans and images from microscopic histopathological examinations will help automatically and accurately classify patients with RD using convolutional neural network (CNN), pre-trained models, and an optimization algorithm on images. This study used the pre-trained CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, and NASNetMobile. In addition, the Sparrow search algorithm (SpaSA) is used to enhance the pre-trained model's performance using the best configuration. Two datasets were used, the first dataset are four classes: cyst, normal, stone, and tumor. In case of the latter, there are five categories within the second dataset that relate to the severity of the tumor: Grade 0, Grade 1, Grade 2, Grade 3, and Grade 4. DenseNet201 and MobileNet pre-trained models are the best for the four-classes dataset compared to others. Besides, the SGD Nesterov parameters optimizer is recommended by three models, while two models only recommend AdaGrad and AdaMax. Among the pre-trained models for the five-class dataset, DenseNet201 and Xception are the best. Experimental results prove the superiority of the proposed framework over other state-of-the-art classification models. The proposed framework records an accuracy of 99.98% (four classes) and 100% (five classes).
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Affiliation(s)
- Mahmoud Badawy
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
- Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah, Saudi Arabia
| | - Abdulqader M Almars
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
| | - Hossam Magdy Balaha
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
- Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, United States
| | - Mohamed Shehata
- Department of Computer Science and Engineering, Speed School of Engineering, University of Louisville, Louisville, KY, United States
| | - Mohammed Qaraad
- Department of Computer Science, Faculty of Science, Amran University, Amran, Yemen
- TIMS, Faculty of Science, Abdelmalek Essaadi University, Tetouan, Morocco
| | - Mostafa Elhosseini
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
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14
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Jiang J, Li J, Li J, Pei H, Li M, Zou Q, Lv Z. A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features. Foods 2023; 12:foods12071498. [PMID: 37048319 PMCID: PMC10094688 DOI: 10.3390/foods12071498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 03/24/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
Umami peptides enhance the umami taste of food and have good food processing properties, nutritional value, and numerous potential applications. Wet testing for the identification of umami peptides is a time-consuming and expensive process. Here, we report the iUmami-DRLF that uses a logistic regression (LR) method solely based on the deep learning pre-trained neural network feature extraction method, unified representation (UniRep based on multiplicative LSTM), for feature extraction from the peptide sequences. The findings demonstrate that deep learning representation learning significantly enhanced the capability of models in identifying umami peptides and predictive precision solely based on peptide sequence information. The newly validated taste sequences were also used to test the iUmami-DRLF and other predictors, and the result indicates that the iUmami-DRLF has better robustness and accuracy and remains valid at higher probability thresholds. The iUmami-DRLF method can aid further studies on enhancing the umami flavor of food for satisfying the need for an umami-flavored diet.
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Affiliation(s)
- Jici Jiang
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Jiayu Li
- College of Life Science, Sichuan University, Chengdu 610065, China
| | - Junxian Li
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Hongdi Pei
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
- Wu Yuzhang Honors College, Sichuan University, Chengdu 610065, China
| | - Mingxin Li
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Zhibin Lv
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
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15
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Perioperative Fast-Track Surgery Nursing Intervention for Patients with Kidney Stone Disease under Computed Tomography Imaging. CONTRAST MEDIA & MOLECULAR IMAGING 2023; 2023:1101388. [PMID: 36793497 PMCID: PMC9925241 DOI: 10.1155/2023/1101388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 08/06/2022] [Accepted: 08/16/2022] [Indexed: 02/10/2023]
Abstract
This research aims to analyze the clinical intervention effect of perioperative fast-track surgery (FTS) nursing on patients with kidney stone disease (KSD) under computed tomography (CT) imaging. One-hundred KSD patients were selected as research objects and grouped after CT examination. These objects were randomly divided into a research group (FTS nursing intervention, n = 50) and a control group (general routine nursing intervention, n = 50). The preoperative psychological status of patients was compared between the two groups, using Self-rating Anxiety Scale and Self-rating Depression Scale. The hunger and thirst situations were compared using Numerical Rating Scale; postoperative recovery time, incidence of complications, and nursing satisfaction were also compared. The high-density shadow could be clearly observed in the right kidney of the patients in the CT imaging examination. The nursing outcomes suggested that there was no notable difference in hunger between the two groups, and anxiety, depression, and thirst in the research group were highly better than those in the control group (P < 0.01). The time of the first exhaust, the time of body temperature returning to normal, the time of getting out of bed, and the length of hospital stay in the research group were all shorter than those in the control group (P < 0.05). The total postoperative satisfaction of the research group (98.00%) was greatly better than the 88.00% in the control group (P < 0.05). As the FTS concept was applied in the perioperative nursing of KSD patients under CT imaging, the preoperative and postoperative negative emotions of patients could be improved. Thereby, the postoperative recovery rate of patients was promoted, postoperative complications and patients' pain were reduced, and the postoperative quality of life of patients was also improved.
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16
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Qadir AM, Abd DF. Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier. KURDISTAN JOURNAL OF APPLIED RESEARCH 2023:131-144. [DOI: 10.24017/science.2022.2.11] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023] Open
Abstract
There are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors related to the nature of the country they live in. Most of the recent studies have focused on investigating prevalent diseases that spread in the population in order to minimize mortality risks, choose the best method for treatment, and improve community healthcare. Kidney disease is one of the most widespread health problems in modern society. This study focuses on kidney stones, cysts, and tumors, the three most common types of renal illness, using a dataset of 12,446 CT urogram and whole abdomen images, aiming to move toward an AI-based kidney disease diagnosis system while contributing to the wider field of artificial intelligence research. In this study, a hybrid technique is used by utilizing both pre-train models for feature extraction and classification using machine learning algorithms for the task of kidney disease image diagnosis. The pre-trained model used in this study is the Densenet-201 model. As well as using Random Forest for classification, the Densenet-201-Random-Forest approach has outperformed many of the previous models used in other studies, having an accuracy rate of 99.719 percent.
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17
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Kahaki FA, Dehnavi SM. Expression Optimizing of Recombinant Oxalyl-CoA Decarboxylase in Escherichia coli. Adv Biomed Res 2022; 11:110. [PMID: 36798915 PMCID: PMC9926027 DOI: 10.4103/abr.abr_244_21] [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: 08/07/2021] [Revised: 12/20/2021] [Accepted: 01/20/2022] [Indexed: 12/28/2022] Open
Abstract
Background One of the most common diseases of the urinary tract is stones of this system, including kidney stones. About 70%-80% of kidney stones are calcium oxalate. Oxalyl-CoA decarboxylase is a single polypeptide included of 568 amino acids which play a key role in oxalate degradation. Materials and Methods The aim of current study is high-level expression of oxalyl-CoA decarboxylase in Escherichia coli BL21 (DE3). To achieve this aim, oxalyl-CoA decarboxylase gene was cloned upon pET-30a (+) with T7 promoter. The vector containing the oxalyl-CoA decarboxylase gene was transformed into E. coli and the expression of the gene was examined on a laboratory scale and fermentor. Atfirst, the effect of temperature, culture medium, and induction time on oxalyl-CoA decarboxylase expression at three levels was examined. Results The obtained data showed that the highest expression was related to the terrific broth culture medium and temperature of 32°C with an inducer concentration of 1 mM. Under this situation the ultimate cells dry weight and the final oxalyl-CoA decarboxylase expression were 2.46 g/l and 36% of total protein, respectively. Then induction time was optimized in a bench bioreactor and productivity of oxalyl-CoA decarboxylase was calculated. Under optimized condition the cell density, biomass productivity and oxalyl-CoA decarboxylase concentration reached 4.02 g/l, 0.22 g/l/h, and 0.7 g/l which are one of the highest reported rates. Conclusion This study demonstrated that high levels of oxalyl-CoA decarboxylase can be achieved by optimizing the expression conditions.
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Affiliation(s)
- Fatemeh Abarghooi Kahaki
- Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Mohsen Dehnavi
- Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran,Address for correspondence: Dr. Seyed Mohsen Dehnavi, Shahid Beheshti University, Daneshjoo Boulevard, Velenjak, Tehran, Iran. E-mail:
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18
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Sassanarakkit S, Hadpech S, Thongboonkerd V. Theranostic roles of machine learning in clinical management of kidney stone disease. Comput Struct Biotechnol J 2022; 21:260-266. [PMID: 36544469 PMCID: PMC9755239 DOI: 10.1016/j.csbj.2022.12.004] [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: 10/11/2022] [Revised: 12/02/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome.
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19
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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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20
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Islam MN, Hasan M, Hossain MK, Alam MGR, Uddin MZ, Soylu A. Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography. Sci Rep 2022; 12:11440. [PMID: 35794172 PMCID: PMC9259587 DOI: 10.1038/s41598-022-15634-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 06/27/2022] [Indexed: 01/15/2023] Open
Abstract
Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community's research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all of them in terms of accuracy, with an accuracy of 99.30 percent. The F1 score and precision and recall comparison reveal that the Swin transformer outperforms all other models and that it is the quickest to train. The study also revealed the blackbox of the VGG16, Resnet50, and Inception models, demonstrating that VGG16 is superior than Resnet50 and Inceptionv3 in terms of monitoring the necessary anatomy abnormalities. We believe that the superior accuracy of our Swin transformer-based model and the VGG16-based model can both be useful in diagnosing kidney tumors, cysts, and stones.
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Affiliation(s)
- Md Nazmul Islam
- grid.52681.380000 0001 0746 8691Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh
| | - Mehedi Hasan
- grid.459397.50000 0004 4682 8575Radiology & Imaging Technology, Bangladesh University of Health Sciences, Dhaka, Bangladesh
| | - Md. Kabir Hossain
- grid.411509.80000 0001 2034 9320Department of Nephrology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
| | - Md. Golam Rabiul Alam
- grid.52681.380000 0001 0746 8691Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh
| | - Md Zia Uddin
- grid.4319.f0000 0004 0448 3150Software and Service Innovation, SINTEF Digital, Oslo, Norway
| | - Ahmet Soylu
- grid.5947.f0000 0001 1516 2393Department of Computer Science, Norwegian University of Science and Technology, Gjøvik, Norway
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21
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Chen D, Li Y. PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features. Front Genet 2022; 13:875112. [PMID: 35547252 PMCID: PMC9081368 DOI: 10.3389/fgene.2022.875112] [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: 02/13/2022] [Accepted: 03/07/2022] [Indexed: 12/03/2022] Open
Abstract
The major histocompatibility complex (MHC) is a large locus on vertebrate DNA that contains a tightly linked set of polymorphic genes encoding cell surface proteins essential for the adaptive immune system. The groups of proteins encoded in the MHC play an important role in the adaptive immune system. Therefore, the accurate identification of the MHC is necessary to understand its role in the adaptive immune system. An effective predictor called PredMHC is established in this study to identify the MHC from protein sequences. Firstly, PredMHC encoded a protein sequence with mixed features including 188D, APAAC, KSCTriad, CKSAAGP, and PAAC. Secondly, three classifiers including SGD, SMO, and random forest were trained on the mixed features of the protein sequence. Finally, the prediction result was obtained by the voting of the three classifiers. The experimental results of the 10-fold cross-validation test in the training dataset showed that PredMHC can obtain 91.69% accuracy. Experimental results on comparison with other features, classifiers, and existing methods showed the effectiveness of PredMHC in predicting the MHC.
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Affiliation(s)
- Dong Chen
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Yanjuan Li
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
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22
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Guan Q, Chen Y, Wei Z, Heidari AA, Hu H, Yang XH, Zheng J, Zhou Q, Chen H, Chen F. Medical image augmentation for lesion detection using a texture-constrained multichannel progressive GAN. Comput Biol Med 2022; 145:105444. [PMID: 35421795 DOI: 10.1016/j.compbiomed.2022.105444] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/31/2021] [Accepted: 03/20/2022] [Indexed: 12/18/2022]
Abstract
Lesion detectors based on deep learning can assist doctors in diagnosing diseases. However, the performance of current detectors is likely to be unsatisfactory due to the scarcity of training samples. Therefore, it is beneficial to use image generation to augment the training set of a detector. However, when the imaging texture of the medical image is relatively delicate, the synthesized image generated by an existing method may be too poor in quality to meet the training requirements of the detectors. In this regard, a medical image augmentation method, namely, a texture-constrained multichannel progressive generative adversarial network (TMP-GAN), is proposed in this work. TMP-GAN uses joint training of multiple channels to effectively avoid the typical shortcomings of the current generation methods. It also uses an adversarial learning-based texture discrimination loss to further improve the fidelity of the synthesized images. In addition, TMP-GAN employs a progressive generation mechanism to steadily improve the accuracy of the medical image synthesizer. Experiments on the publicly available dataset CBIS-DDMS and our pancreatic tumor dataset show that the precision/recall/F1-score of the detector trained on the TMP-GAN augmented dataset improves by 2.59%/2.70%/2.77% and 2.44%/2.06%/2.36%, respectively, compared to the optimal results of other data augmentation methods. The FROC curve of the detector is also better than the curve from the contrast-augmented trained dataset. Therefore, we believe the proposed TMP-GAN is a practical technique to efficiently implement lesion detection case studies.
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Affiliation(s)
- Qiu Guan
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Yizhou Chen
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Zihan Wei
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Haigen Hu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Xu-Hua Yang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Jianwei Zheng
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Qianwei Zhou
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China.
| | - Feng Chen
- The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Abstract
PURPOSE OF REVIEW Artificial intelligence in medicine has allowed for efficient processing of large datasets to perform cognitive tasks that facilitate clinical decision-making, and it is an emerging area of research. This review aims to highlight the most pertinent and recent research in artificial intelligence in endourology, where it has been used to optimize stone diagnosis, support decision-making regarding management, predict stone recurrence, and provide new tools for bioinformatics research within endourology. RECENT FINDINGS Artificial neural networks (ANN) and machine learning approaches have demonstrated high accuracy in predicting stone diagnoses, stone composition, and outcomes of spontaneous stone passage, shockwave lithotripsy (SWL), or percutaneous nephrolithotomy (PCNL); some of these models outperform more traditional predictive models and existing nomograms. In addition, these approaches have been used to predict stone recurrence, quality of life scores, and provide novel methods of mining the electronic medical record for research. SUMMARY Artificial intelligence can be used to enhance existing approaches to stone diagnosis, management, and prevention to provide a more individualized approach to endourologic care. Moreover, it may support an emerging area of bioinformatics research within endourology. However, despite high accuracy, many of the published algorithms lack external validity and require further study before they are more widely adopted.
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