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Yuan G, Cai L, Qu W, Zhou Z, Liang P, Chen J, Xu C, Zhang J, Wang S, Chu Q, Li Z. Identification of Calculous Pyonephrosis by CT-Based Radiomics and Deep Learning. Bioengineering (Basel) 2024; 11:662. [PMID: 39061744 PMCID: PMC11274102 DOI: 10.3390/bioengineering11070662] [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: 06/06/2024] [Revised: 06/18/2024] [Accepted: 06/22/2024] [Indexed: 07/28/2024] Open
Abstract
Urgent detection of calculous pyonephrosis is crucial for surgical planning and preventing severe outcomes. This study aims to evaluate the performance of computed tomography (CT)-based radiomics and a three-dimensional convolutional neural network (3D-CNN) model, integrated with independent clinical factors, to identify patients with calculous pyonephrosis. We recruited 182 patients receiving either percutaneous nephrostomy tube placement or percutaneous nephrolithotomy for calculous hydronephrosis or pyonephrosis. The regions of interest were manually delineated on plain CT images and the CT attenuation value (HU) was measured. Radiomics analysis was performed using least absolute shrinkage and selection operator (LASSO). A 3D-CNN model was also developed. The better-performing machine-learning model was combined with independent clinical factors to build a comprehensive clinical machine-learning model. The performance of these models was assessed using receiver operating characteristic analysis and decision curve analysis. Fever, blood neutrophils, and urine leukocytes were independent risk factors for pyonephrosis. The radiomics model showed higher area under the curve (AUC) than the 3D-CNN model and HU (0.876 vs. 0.599, 0.578; p = 0.003, 0.002) in the testing cohort. The clinical machine-learning model surpassed the clinical model in both the training (0.975 vs. 0.904, p = 0.019) and testing (0.967 vs. 0.889, p = 0.045) cohorts.
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Affiliation(s)
- Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Lingli Cai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Weinuo Qu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Ziling Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Jun Chen
- Bayer Healthcare, Wuhan 430000, China;
| | - Chuou Xu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Jiaqiao Zhang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Shaogang Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Qian Chu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
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Waldner S, Wendelspiess E, Detampel P, Schlepütz CM, Huwyler J, Puchkov M. Advanced analysis of disintegrating pharmaceutical compacts using deep learning-based segmentation of time-resolved micro-tomography images. Heliyon 2024; 10:e26025. [PMID: 38384517 PMCID: PMC10878950 DOI: 10.1016/j.heliyon.2024.e26025] [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: 07/23/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/23/2024] Open
Abstract
The mechanism governing pharmaceutical tablet disintegration is far from fully understood. Despite the importance of controlling a formulation's disintegration process to maximize the active pharmaceutical ingredient's bioavailability and ensure predictable and consistent release profiles, the current understanding of the process is based on indirect or superficial measurements. Formulation science could, therefore, additionally deepen the understanding of the fundamental physical principles governing disintegration based on direct observations of the process. We aim to help bridge the gap by generating a series of time-resolved X-ray micro-computed tomography (μCT) images capturing volumetric images of a broad range of mini-tablet formulations undergoing disintegration. Automated image segmentation was a prerequisite to overcoming the challenges of analyzing multiple time series of heterogeneous tomographic images at high magnification. We devised and trained a convolutional neural network (CNN) based on the U-Net architecture for autonomous, rapid, and consistent image segmentation. We created our own μCT data reconstruction pipeline and parameterized it to deliver image quality optimal for our CNN-based segmentation. Our approach enabled us to visualize the internal microstructures of the tablets during disintegration and to extract parameters of disintegration kinetics from the time-resolved data. We determine by factor analysis the influence of the different formulation components on the disintegration process in terms of both qualitative and quantitative experimental responses. We relate our findings to known formulation component properties and established experimental results. Our direct imaging approach, enabled by deep learning-based image processing, delivers new insights into the disintegration mechanism of pharmaceutical tablets.
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Affiliation(s)
- Samuel Waldner
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | - Erwin Wendelspiess
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | - Pascal Detampel
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | | | - Jörg Huwyler
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | - Maxim Puchkov
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
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Sheikhi M, Sina S, Karimipourfard M. Deep-learned generation of renal dual-energy CT from a single-energy scan. Clin Radiol 2024; 79:e17-e25. [PMID: 37923626 DOI: 10.1016/j.crad.2023.09.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 09/14/2023] [Accepted: 09/24/2023] [Indexed: 11/07/2023]
Abstract
AIM To investigate the role of the deep-learning (DL) method in the generation of dual-energy computed tomography (DECT) images from single-energy images for precise diagnosis of kidney stone type. MATERIALS AND METHODS DECT of 23 patients was acquired, and the stone types were investigated based on the DECT software suggestions. The data were divided into two paired groups:120 kVp input and 80 kVp target and 120 kVp input and 135 kVp targets, p2p-UNet-GAN was exploited to generate the different energy images based on the common CT protocols. RESULTS The images generated of the generative adversarial network (GAN) network were evaluated based on the SSIM, PSNR, and MSE metrics, and the values were estimated as 0.85-0.95, 28-32, and 0.85-0.89 respectively. The attenuation ratio of test patient images were estimated and compared with real patient reports. The network achieved high accuracy in stone region localisation and resulted in accurate stone type predictions. CONCLUSION This study presents a useful method based on the DL technique to reduce patient radiation dose and facilitate the prediction of urinary stone types using single-energy CT imaging.
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Affiliation(s)
- M Sheikhi
- Nuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran; Abu Ali Sina Hospital, Shiraz, Iran
| | - S Sina
- Nuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran; Radiation Research Center, Shiraz University, Shiraz, Iran.
| | - M Karimipourfard
- Nuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran
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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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Patro KK, Allam JP, Neelapu BC, Tadeusiewicz R, Acharya UR, Hammad M, Yildirim O, Pławiak P. Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images. Inf Sci (N Y) 2023; 640:119005. [DOI: 10.1016/j.ins.2023.119005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
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6
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Huang ZH, Liu YY, Wu WJ, Huang KW. Design and Validation of a Deep Learning Model for Renal Stone Detection and Segmentation on Kidney-Ureter-Bladder Images. Bioengineering (Basel) 2023; 10:970. [PMID: 37627855 PMCID: PMC10452034 DOI: 10.3390/bioengineering10080970] [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: 06/27/2023] [Revised: 08/11/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023] Open
Abstract
Kidney-ureter-bladder (KUB) imaging is used as a frontline investigation for patients with suspected renal stones. In this study, we designed a computer-aided diagnostic system for KUB imaging to assist clinicians in accurately diagnosing urinary tract stones. The image dataset used for training and testing the model comprised 485 images provided by Kaohsiung Chang Gung Memorial Hospital. The proposed system was divided into two subsystems, 1 and 2. Subsystem 1 used Inception-ResNetV2 to train a deep learning model on preprocessed KUB images to verify the improvement in diagnostic accuracy with image preprocessing. Subsystem 2 trained an image segmentation model using the ResNet hybrid, U-net, to accurately identify the contours of renal stones. The performance was evaluated using a confusion matrix for the classification model. We conclude that the model can assist clinicians in accurately diagnosing renal stones via KUB imaging. Therefore, the proposed system can assist doctors in diagnosis, reduce patients' waiting time for CT scans, and minimize the radiation dose absorbed by the body.
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Affiliation(s)
- Zih-Hao Huang
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan; (Z.-H.H.); (Y.-Y.L.); (W.-J.W.)
| | - Yi-Yang Liu
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan; (Z.-H.H.); (Y.-Y.L.); (W.-J.W.)
- Department of Urology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan
| | - Wei-Juei Wu
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan; (Z.-H.H.); (Y.-Y.L.); (W.-J.W.)
| | - Ko-Wei Huang
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan; (Z.-H.H.); (Y.-Y.L.); (W.-J.W.)
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Anastasiadis A, Koudonas A, Langas G, Tsiakaras S, Memmos D, Mykoniatis I, Symeonidis EN, Tsiptsios D, Savvides E, Vakalopoulos I, Dimitriadis G, de la Rosette J. Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review. Asian J Urol 2023; 10:258-274. [PMID: 37538159 PMCID: PMC10394286 DOI: 10.1016/j.ajur.2023.02.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/23/2022] [Accepted: 02/10/2023] [Indexed: 08/05/2023] Open
Abstract
Objective To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease. Methods A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ''endourology'', ''artificial intelligence'', ''machine learning'', and ''urolithiasis'' were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language. Results A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors. Conclusion AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.
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Affiliation(s)
- Anastasios Anastasiadis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Antonios Koudonas
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Georgios Langas
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Stavros Tsiakaras
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Dimitrios Memmos
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Ioannis Mykoniatis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Evangelos N. Symeonidis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Dimitrios Tsiptsios
- Neurology Department, Democritus University of Thrace, Alexandroupolis, Greece
| | | | - Ioannis Vakalopoulos
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Georgios Dimitriadis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Jean de la Rosette
- Department of Urology, Istanbul Medipol Mega University Hospital, Istanbul, Turkey
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Liu YY, Huang ZH, Huang KW. Deep Learning Model for Computer-Aided Diagnosis of Urolithiasis Detection from Kidney-Ureter-Bladder Images. Bioengineering (Basel) 2022; 9:811. [PMID: 36551017 PMCID: PMC9774756 DOI: 10.3390/bioengineering9120811] [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: 11/10/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
Kidney-ureter-bladder (KUB) imaging is a radiological examination with a low cost, low radiation, and convenience. Although emergency room clinicians can arrange KUB images easily as a first-line examination for patients with suspicious urolithiasis, interpreting the KUB images correctly is difficult for inexperienced clinicians. Obtaining a formal radiology report immediately after a KUB imaging examination can also be challenging. Recently, artificial-intelligence-based computer-aided diagnosis (CAD) systems have been developed to help clinicians who are not experts make correct diagnoses for further treatment more effectively. Therefore, in this study, we proposed a CAD system for KUB imaging based on a deep learning model designed to help first-line emergency room clinicians diagnose urolithiasis accurately. A total of 355 KUB images were retrospectively collected from 104 patients who were diagnosed with urolithiasis at Kaohsiung Chang Gung Memorial Hospital. Then, we trained a deep learning model with a ResNet architecture to classify KUB images in terms of the presence or absence of kidney stones with this dataset of pre-processed images. Finally, we tuned the parameters and tested the model experimentally. The results show that the accuracy, sensitivity, specificity, and F1-measure of the model were 0.977, 0.953, 1, and 0.976 on the validation set and 0.982, 0.964, 1, and 0.982 on the testing set, respectively. Moreover, the results demonstrate that the proposed model performed well compared to the existing CNN-based methods and was able to detect urolithiasis in KUB images successfully. We expect the proposed approach to help emergency room clinicians make accurate diagnoses and reduce unnecessary radiation exposure from computed tomography (CT) scans, along with the associated medical costs.
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Affiliation(s)
- Yi-Yang Liu
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 80778, Taiwan
- Department of Urology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan
| | - Zih-Hao Huang
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 80778, Taiwan
| | - Ko-Wei Huang
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 80778, Taiwan
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Bharati A, Rani Mandal S, Gupta AK, Seth A, Sharma R, Bhalla AS, Das CJ, Chatterjee S, Kumar P. Non-Invasive characterisation of renal stones using dual energy CT: A method to differentiate calcium stones. Phys Med 2022; 101:158-164. [PMID: 36007404 DOI: 10.1016/j.ejmp.2022.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/22/2022] [Accepted: 08/17/2022] [Indexed: 10/15/2022] Open
Abstract
BACKGROUND Non-invasive DECT based characterization of renal stones using their effective atomic number (Zeff) and the electron density (ρe) in patients. AIM This paper aims to develop a method for in-vivo characterization of renal stone. Differentiation of renal stones in-vivo especially sub types of calcium stones have very important advantage for better judgement of treatment modality. MATERIALS AND METHODS 50 extracted renal stones were scanned ex-vivo using dual energy CT scanner. A method was developed to characterize these renal stones using effective atomic number and electron density obtained from dual energy CT data. The method and formulation developed in ex-vivo experiments was applied in in-vivo study of 50 randomly selected patients of renal stones who underwent dual energy CT scan. RESULTS The developed method was able to characterize Calcium Oxalate Monohydrate (COM) and the combination of COM and Calcium Oxalate Dihydrate (COD) stones non-invasively in patients with a sensitivity of 81% and 83%respectively. The method was also capable of differentiating Uric, Cystine and mixed stones with the sensitivity of 100, 100 and 85.71% respectively. CONCLUSION The developed dual energy CT based method was capable of differentiating sub types of calcium stones which is not differentiable on single energy or dual energy CT images.
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Affiliation(s)
- Avinav Bharati
- Department of Radiation Oncology, Dr. Ram Manohar Lohia Institute of Medical Sciences, Lucknow, Uttar Prades 226010, India
| | | | | | - Amlesh Seth
- Department of Urology, AIIMS, New Delhi 110029, India
| | - Raju Sharma
- Department of Radiodiagnosis, AIIMS, New Delhi 110029, India
| | - Ashu S Bhalla
- Department of Radiodiagnosis, AIIMS, New Delhi 110029, India
| | - Chandan J Das
- Department of Radiodiagnosis, AIIMS, New Delhi 110029, India
| | - Sabyasachi Chatterjee
- BGVS, Chemical Engineering Building (Old), Institute of Science, Bengaluru, Karnataka 560012,India
| | - Pratik Kumar
- Medical Physics Unit, IRCH, AIIMS, New Delhi 110029, India.
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Deep learning model for automated kidney stone detection using coronal CT images. Comput Biol Med 2021; 135:104569. [PMID: 34157470 DOI: 10.1016/j.compbiomed.2021.104569] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/01/2021] [Accepted: 06/09/2021] [Indexed: 11/23/2022]
Abstract
Kidney stones are a common complaint worldwide, causing many people to admit to emergency rooms with severe pain. Various imaging techniques are used for the diagnosis of kidney stone disease. Specialists are needed for the interpretation and full diagnosis of these images. Computer-aided diagnosis systems are the practical approaches that can be used as auxiliary tools to assist the clinicians in their diagnosis. In this study, an automated detection of kidney stone (having stone/not) using coronal computed tomography (CT) images is proposed with deep learning (DL) technique which has recently made significant progress in the field of artificial intelligence. A total of 1799 images were used by taking different cross-sectional CT images for each person. Our developed automated model showed an accuracy of 96.82% using CT images in detecting the kidney stones. We have observed that our model is able to detect accurately the kidney stones of even small size. Our developed DL model yielded superior results with a larger dataset of 433 subjects and is ready for clinical application. This study shows that recently popular DL methods can be employed to address other challenging problems in urology.
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