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Panthier F, Melchionna A, Crawford-Smith H, Phillipou Y, Choong S, Arumuham V, Allen S, Allen C, Smith D. Can Artificial Intelligence Accurately Detect Urinary Stones? A Systematic Review. J Endourol 2024; 38:725-740. [PMID: 38666692 DOI: 10.1089/end.2023.0717] [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: 06/01/2024] Open
Abstract
Objectives: To perform a systematic review on artificial intelligence (AI) performances to detect urinary stones. Methods: A PROSPERO-registered (CRD473152) systematic search of Scopus, Web of Science, Embase, and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms ("automatic" OR "machine learning" OR "convolutional neural network" OR "artificial intelligence" OR "detection" AND "stone volume"). Risk-of-bias (RoB) assessment was performed according to the Cochrane RoB tool, the Joanna Briggs Institute Checklist for nonrandomized studies, and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: Twelve studies were selected for the final review, including three multicenter and nine single-center retrospective studies. Eleven studies completed at least 50% of the CLAIM checkpoints and only one presented a high RoB. All included studies aimed to detect kidney (5/12, 42%), ureter (2/12, 16%), or urinary (5/12, 42%) stones on noncontrast computed tomography (NCCT), but 42% intended to automate measurement. Stone distinction from vascular calcification interested two studies. All studies used AI machine learning network training and internal validation, but a single one provided an external validation. Trained networks achieved stone detection, with sensitivity, specificity, and accuracy rates ranging from 58.7% to 100%, 68.5% to 100%, and 63% to 99.95%, respectively. Detection Dice score ranged from 83% to 97%. A high correlation between manual and automated stone volume (r = 0.95) was noted. Differentiate distal ureteral stones and phleboliths seemed feasible. Conclusions: AI processes can achieve automated urinary stone detection from NCCT. Further studies should provide urinary stone detection coupled with phlebolith distinction and an external validation, and include anatomical abnormalities and urologic foreign bodies (ureteral stent and nephrostomy tubes) cases.
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Affiliation(s)
- Frédéric Panthier
- Department of Urology, Westmoreland Street Hospital, UCLH NHS Foundation Trust, London, United Kingdom
- Sorbonne University GRC Urolithiasis No. 20 Tenon Hospital, Paris, France
- Progressive Endourological Association for Research and Leading Solutions (PEARLS), Paris, France
- PIMM, UMR 8006 CNRS-Arts et Métiers ParisTech, Paris, France
| | - Alberto Melchionna
- Department of Urology, Westmoreland Street Hospital, UCLH NHS Foundation Trust, London, United Kingdom
| | - Hugh Crawford-Smith
- Department of Urology, Westmoreland Street Hospital, UCLH NHS Foundation Trust, London, United Kingdom
| | - Yiannis Phillipou
- Department of Urology, Westmoreland Street Hospital, UCLH NHS Foundation Trust, London, United Kingdom
| | - Simon Choong
- Department of Urology, Westmoreland Street Hospital, UCLH NHS Foundation Trust, London, United Kingdom
| | - Vimoshan Arumuham
- Department of Urology, Westmoreland Street Hospital, UCLH NHS Foundation Trust, London, United Kingdom
| | - Sian Allen
- Department of Urology, Westmoreland Street Hospital, UCLH NHS Foundation Trust, London, United Kingdom
| | - Clare Allen
- Department of Radiology, University College London Hospitals, London, United Kingdom
| | - Daron Smith
- Department of Urology, Westmoreland Street Hospital, UCLH NHS Foundation Trust, London, United Kingdom
- Endourology Academy
- Social Media Committee, Endourological Society
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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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Koç U, Sezer EA, Özkaya YA, Yarbay Y, Beşler MS, Taydaş O, Yalçın A, Evrimler Ş, Kızıloğlu HA, Kesimal U, Atasoy D, Oruç M, Ertuğrul M, Karakaş E, Karademir F, Sebik NB, Topuz Y, Aktan ME, Sezer Ö, Aydın Ş, Varlı S, Akdoğan E, Ülgü MM, Birinci Ş. Elevating healthcare through artificial intelligence: analyzing the abdominal emergencies data set (TR_ABDOMEN_RAD_EMERGENCY) at TEKNOFEST-2022. Eur Radiol 2024; 34:3588-3597. [PMID: 37947834 DOI: 10.1007/s00330-023-10391-y] [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: 01/17/2023] [Revised: 08/28/2023] [Accepted: 09/08/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVES The artificial intelligence competition in healthcare at TEKNOFEST-2022 provided a platform to address the complex multi-class classification challenge of abdominal emergencies using computer vision techniques. This manuscript aimed to comprehensively present the methodologies for data preparation, annotation procedures, and rigorous evaluation metrics. Moreover, it was conducted to introduce a meticulously curated abdominal emergencies data set to the researchers. METHODS The data set underwent a comprehensive central screening procedure employing diverse algorithms extracted from the e-Nabız (Pulse) and National Teleradiology System of the Republic of Türkiye, Ministry of Health. Full anonymization of the data set was conducted. Subsequently, the data set was annotated by a group of ten experienced radiologists. The evaluation process was executed by calculating F1 scores, which were derived from the intersection over union values between the predicted bounding boxes and the corresponding ground truth (GT) bounding boxes. The establishment of baseline performance metrics involved computing the average of the highest five F1 scores. RESULTS Observations indicated a progressive decline in F1 scores as the threshold value increased. Furthermore, it could be deduced that class 6 (abdominal aortic aneurysm/dissection) was relatively straightforward to detect compared to other classes, with class 5 (acute diverticulitis) presenting the most formidable challenge. It is noteworthy, however, that if all achieved outcomes for all classes were considered with a threshold of 0.5, the data set's complexity and associated challenges became pronounced. CONCLUSION This data set's significance lies in its pioneering provision of labels and GT-boxes for six classes, fostering opportunities for researchers. CLINICAL RELEVANCE STATEMENT The prompt identification and timely intervention in cases of emergent medical conditions hold paramount significance. The handling of patients' care can be augmented, while the potential for errors is minimized, particularly amidst high caseload scenarios, through the application of AI. KEY POINTS • The data set used in artificial intelligence competition in healthcare (TEKNOFEST-2022) provides a 6-class data set of abdominal CT images consisting of a great variety of abdominal emergencies. • This data set is compiled from the National Teleradiology System data repository of emergency radiology departments of 459 hospitals. • Radiological data on abdominal emergencies is scarce in literature and this annotated competition data set can be a valuable resource for further studies and new AI models.
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Affiliation(s)
- Ural Koç
- Department of Radiology, Ankara Bilkent City Hospital, Ankara, Türkiye.
| | - Ebru Akçapınar Sezer
- Artificial Intelligence Division, Department of Computer Engineering, Hacettepe University, Ankara, Türkiye
| | | | - Yasin Yarbay
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | | | - Onur Taydaş
- Department of Radiology, Faculty of Medicine, Sakarya University, Sakarya, Türkiye
| | - Ahmet Yalçın
- Department of Radiology, Faculty of Medicine, Erzurum Atatürk University, Erzurum, Türkiye
| | - Şehnaz Evrimler
- Department of Radiology, Ankara Etlik City Hospital, Ankara, Türkiye
| | | | - Uğur Kesimal
- Department of Radiology, Ankara Training and Research Hospital, Ankara, Türkiye
| | - Dilara Atasoy
- Department of Radiology, Sivas Numune State Hospital, Sivas, Türkiye
| | - Meltem Oruç
- Department of Radiology, Karaman Training and Research Hospital, Karaman, Türkiye
| | - Mustafa Ertuğrul
- Department of Radiology, Ürgüp State Hospital, Nevşehir, Türkiye
| | - Emrah Karakaş
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | | | - Nihat Barış Sebik
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | | | | | - Özgür Sezer
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | - Şahin Aydın
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | - Songül Varlı
- Health Institutes of Türkiye, İstanbul, Türkiye
- Department of Computer Engineering, Yıldız Technical University, İstanbul, Türkiye
| | - Erhan Akdoğan
- Health Institutes of Türkiye, İstanbul, Türkiye
- Department of Mechatronics Engineering, Faculty of Mechanical Engineering, Yıldız Technical University, İstanbul, Türkiye
| | - Mustafa Mahir Ülgü
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
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Chen J, Chen R, Chen L, Zhang L, Wang W, Zeng X. Kidney medicine meets computer vision: a bibliometric analysis. Int Urol Nephrol 2024:10.1007/s11255-024-04082-w. [PMID: 38814370 DOI: 10.1007/s11255-024-04082-w] [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: 02/27/2024] [Accepted: 05/16/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND AND OBJECTIVE Rapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine research. METHODS The Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and Vosviewer. RESULTS There was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data modalities. CONCLUSION The present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key sources, leading institutions, and popular topics. Our findings and analysis are expected to provide valuable insights for future research on the use of CV in kidney medicine research.
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Affiliation(s)
- Junren Chen
- Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Rui Chen
- The Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
| | - Liangyin Chen
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Lei Zhang
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Wei Wang
- School of Automation, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China
| | - Xiaoxi Zeng
- Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, Sichuan, China.
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Vigneswaran G, Teh R, Ripa F, Pietropaolo A, Modi S, Chauhan J, Somani BK. A machine learning approach using stone volume to predict stone-free status at ureteroscopy. World J Urol 2024; 42:344. [PMID: 38775943 DOI: 10.1007/s00345-024-05054-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/09/2024] [Indexed: 08/23/2024] Open
Abstract
INTRODUCTION To develop a predictive model incorporating stone volume along with other clinical and radiological factors to predict stone-free (SF) status at ureteroscopy (URS). MATERIAL AND METHODS Retrospective analysis of patients undergoing URS for kidney stone disease at our institution from 2012 to 2021. SF status was defined as stone fragments < 2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments > 2 mm at XR KUB or US KUB at 3 months follow up. We specifically included all non-SF patients to optimise our algorithm for identifying instances with residual stone burden. SF patients were also randomly sampled over the same time period to ensure a more balanced dataset for ML prediction. Stone volumes were measured using preprocedural CT and combined with 19 other clinical and radiological factors. A bagged trees machine learning model with cross-validation was used for this analysis. RESULTS 330 patients were included (SF: n = 276, not SF: n = 54, mean age 59.5 ± 16.1 years). A fivefold cross validated RUSboosted trees model has an accuracy of 74.5% and AUC of 0.82. The model sensitivity and specificity were 75% and 72.2% respectively. Variable importance analysis identified total stone volume (17.7% of total importance), operation time (14.3%), age (12.9%) and stone composition (10.9%) as important factors in predicting non-SF patients. Single and cumulative stone size which are commonly used in current practice to guide management, only represented 9.4% and 4.7% of total importance, respectively. CONCLUSION Machine learning can be used to predict patients that will be SF at the time of URS. Total stone volume appears to be more important than stone size in predicting SF status. Our findings could be used to optimise patient counselling and highlight an increasing role of stone volume to guide endourological practice and future guidelines.
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Affiliation(s)
- Ganesh Vigneswaran
- Department of Interventional Radiology, University Hospital Southampton, Southampton, UK
- Cancer Sciences, University of Southampton, Southampton, UK
| | - Ren Teh
- Department of Interventional Radiology, University Hospital Southampton, Southampton, UK
| | - Francesco Ripa
- Department of Urology, University Hospital Southampton, Tremona Road, Southampton, UK
| | - Amelia Pietropaolo
- Department of Urology, University Hospital Southampton, Tremona Road, Southampton, UK
| | - Sachin Modi
- Department of Interventional Radiology, University Hospital Southampton, Southampton, UK
| | - Jagmohan Chauhan
- Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Bhaskar Kumar Somani
- Department of Urology, University Hospital Southampton, Tremona Road, Southampton, UK.
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Yao J, Chu LC, Patlas M. Applications of Artificial Intelligence in Acute Abdominal Imaging. Can Assoc Radiol J 2024:8465371241250197. [PMID: 38715249 DOI: 10.1177/08465371241250197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of these situations renders emergent abdominal imaging an ideal candidate for AI applications. CT, radiographs, and ultrasound are the most common modalities for imaging evaluation of these patients. For each modality, numerous studies have assessed the performance of AI models for detecting common pathologies, such as appendicitis, bowel obstruction, and cholecystitis. The capabilities of these models range from simple classification to detailed severity assessment. This narrative review explores the evolution, trends, and challenges in AI applications for evaluating acute abdominal pathologies. We review implementations of AI for non-traumatic and traumatic abdominal pathologies, with discussion of potential clinical impact, challenges, and future directions for the technology.
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Affiliation(s)
- Jason Yao
- Department of Radiology, McMaster University, Hamilton, ON, Canada
| | - Linda C Chu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael Patlas
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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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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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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De Coninck V, Skolarikos A, Juliebø-Jones P, Joris M, Traxer O, Keller EX. Advancements in stone classification: unveiling the beauty of urolithiasis. World J Urol 2024; 42:46. [PMID: 38244083 DOI: 10.1007/s00345-023-04746-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 11/02/2023] [Indexed: 01/22/2024] Open
Abstract
PURPOSE Urolithiasis has become increasingly prevalent, leading to higher disability-adjusted life years and deaths. Various stone classification systems have been developed to enhance the understanding of lithogenesis, aid urologists in treatment decisions, and predict recurrence risk. The aim of this manuscript is to provide an overview of different stone classification criteria. METHODS Two authors conducted a review of literature on studies relating to the classification of urolithiasis. A narrative synthesis for analysis of the studies was used. RESULTS Stones can be categorized based on anatomical position, size, medical imaging features, risk of recurrence, etiology, composition, and morphoconstitutional analysis. The first three mentioned offer a straightforward approach to stone classification, directly influencing treatment recommendations. With the routine use of CT imaging before treatment, precise details like anatomical location, stone dimensions, and Hounsfield Units can be easily determined, aiding treatment planning. In contrast, classifying stones based on risk of recurrence and etiology is more complex due to dependencies on multiple variables, including stone composition and morphology. A classification system based on morphoconstitutional analysis, which combines morphological stone appearance and chemical composition, has demonstrated its value. It allows for the rapid identification of crystalline phase principles, the detection of crystalline conversion processes, the determination of etiopathogenesis, the recognition of lithogenic processes, the assessment of crystal formation speed, related recurrence rates, and guidance for selecting appropriate treatment modalities. CONCLUSIONS Recognizing that no single classification system can comprehensively cover all aspects, the integration of all classification approaches is essential for tailoring urolithiasis patient-specific management.
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Affiliation(s)
- Vincent De Coninck
- Department of Urology, Augustijnslei 100, Klina, 2930, Brasschaat, AZ, Belgium.
- Young Academic Urologists (YAU), Urolithiasis and Endourology Working Party, Arnhem, The Netherlands.
| | - Andreas Skolarikos
- Department of Urology, National and Kapodistrian University of Athens, Athens, Greece
| | | | - Manu Joris
- Faculty of Medicine, University Hospitals Leuven, Louvain, Belgium
| | - Olivier Traxer
- GRC N°20, Groupe de Recherche Clinique sur la Lithiase Urinaire, Hôpital Tenon, Sorbonne Université, Arnhem, The Netherlands
- Service d'Urologie, Assistance-Publique Hôpitaux de Paris, Hôpital Tenon, Sorbonne Université, Arnhem, The Netherlands
| | - Etienne Xavier Keller
- Young Academic Urologists (YAU), Urolithiasis and Endourology Working Party, Arnhem, The Netherlands
- Department of Urology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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11
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Zhu G, Li C, Guo Y, Sun L, Jin T, Wang Z, Li S, Zhou F. Predicting stone composition via machine-learning models trained on intra-operative endoscopic digital images. BMC Urol 2024; 24:5. [PMID: 38172816 PMCID: PMC10765800 DOI: 10.1186/s12894-023-01396-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/27/2023] [Indexed: 01/05/2024] Open
Abstract
OBJECTIVES The aim of this study was to use deep learning (DL) of intraoperative images of urinary stones to predict the composition of urinary stones. In this way, the laser frequency and intensity can be adjusted in real time to reduce operation time and surgical trauma. MATERIALS AND METHODS A total of 490 patients who underwent holmium laser surgery during the two-year period from March 2021 to March 2023 and had stone analysis results were collected by the stone laboratory. A total of 1658 intraoperative stone images were obtained. The eight stone categories with the highest number of stones were selected by sorting. Single component stones include calcium oxalate monohydrate (W1), calcium oxalate dihydrate (W2), magnesium ammonium phosphate hexahydrate, apatite carbonate (CH) and anhydrous uric acid (U). Mixed stones include W2 + U, W1 + W2 and W1 + CH. All stones have intraoperative videos. More than 20 intraoperative high-resolution images of the stones, including the surface and core of the stones, were available for each patient via FFmpeg command screenshots. The deep convolutional neural network (CNN) ResNet-101 (ResNet, Microsoft) was applied to each image as a multiclass classification model. RESULTS The composition prediction rates for each component were as follows: calcium oxalate monohydrate 99% (n = 142), calcium oxalate dihydrate 100% (n = 29), apatite carbonate 100% (n = 131), anhydrous uric acid 98% (n = 57), W1 + W2 100% (n = 82), W1 + CH 100% ( n = 20) and W2 + U 100% (n = 24). The overall weighted recall of the cellular neural network component analysis for the entire cohort was 99%. CONCLUSION This preliminary study suggests that DL is a promising method for identifying urinary stone components from intraoperative endoscopic images. Compared to intraoperative identification of stone components by the human eye, DL can discriminate single and mixed stone components more accurately and quickly. At the same time, based on the training of stone images in vitro, it is closer to the clinical application of stone images in vivo. This technology can be used to identify the composition of stones in real time and to adjust the frequency and energy intensity of the holmium laser in time. The prediction of stone composition can significantly shorten the operation time, improve the efficiency of stone surgery and prevent the risk of postoperative infection.
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Affiliation(s)
- Guanhua Zhu
- Department of Urology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Soochow, 215006, Jiangsu Province, China
| | - Chengbai Li
- Department of Urology, Wuxi 9th People's Hospital Affiliated to Soochow University, 999 Liangxi Road, Wuxi, 214000, Jiangsu Province, China
| | - Yinsheng Guo
- Department of Urology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Soochow, 215006, Jiangsu Province, China
| | - Lu Sun
- Department of Urology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Soochow, 215006, Jiangsu Province, China
| | - Tao Jin
- Mingxu Technology Co., Ltd., 1228 Jiangchang Road, Shanghai, 200072, China
| | - Ziyue Wang
- Mingxu Technology Co., Ltd., 1228 Jiangchang Road, Shanghai, 200072, China
| | - Shiqing Li
- Department of Urology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Soochow, 215006, Jiangsu Province, China.
| | - Feng Zhou
- Department of Urology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Soochow, 215006, Jiangsu Province, China.
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Panthier F, Kutchukian S, Ducousso H, Doizi S, Solano C, Candela L, Corrales M, Chicaud M, Traxer O, Hautekeete S, Tailly T. How to estimate stone volume and its use in stone surgery: a comprehensive review. Actas Urol Esp 2024; 48:71-78. [PMID: 37657708 DOI: 10.1016/j.acuroe.2023.08.009] [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: 04/24/2023] [Accepted: 07/10/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVE Current interventional guidelines refer to the cumulative stone diameter to choose the appropriate surgical modality (ureteroscopy [URS], extracorporeal shockwave lithotripsy [ESWL] and percutaneous nephrolithotomy [PCNL]). The stone volume (SV) has been introduced recently, to better estimate the stone burden. This review aimed to summarize the available methods to evaluate the SV and its use in urolithiasis treatment. MATERIAL AND METHODS A comprehensive review of the literature was performed in December 2022 by searching Embase, Cochrane and Pubmed databases. Articles were considered eligible if they described SV measurement or the stone free rate after different treatment modalities (SWL, URS, PCNL) or spontaneous passage, based on SV measurement. Two reviewers independently assessed the eligibility and the quality of the articles and performed the data extraction. RESULTS In total, 28 studies were included. All studies used different measurement techniques for stone volume. The automated volume measurement appeared to be more precise than the calculated volume. In vitro studies showed that the automated volume measurement was closer to actual stone volume, with a lower inter-observer variability. Regarding URS, stone volume was found to be more predictive of stone free rates as compared to maximum stone diameter or cumulative diameter for stones >20 mm. This was not the case for PCNL and SWL. CONCLUSIONS Stone volume estimation is feasible, manually or automatically and is likely a better representation of the actual stone burden. While for larger stones treated by retrograde intrarenal surgery, stone volume appears to be a better predictor of SFR, the superiority of stone volume throughout all stone burdens and for all stone treatments, remains to be proven. Automated volume acquisition is more precise and reproducible than calculated volume.
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Affiliation(s)
- F Panthier
- Grupo de Investigación Clínica en Litiasis Urinaria, Hospital Tenon, Paris, France; Servicio de Urología, Asistencia Pública Hospitales de París, Hospital Tenon, Universidad de La Sorbona, Paris, France.
| | - S Kutchukian
- Grupo de Investigación Clínica en Litiasis Urinaria, Hospital Tenon, Paris, France; Servicio de Urología, Asistencia Pública Hospitales de París, Hospital Tenon, Universidad de La Sorbona, Paris, France; Servicio de Urología, Hospital Universitario de Poitiers, Poitiers, France
| | - H Ducousso
- Servicio de Urología, Hospital Universitario de Poitiers, Poitiers, France
| | - S Doizi
- Grupo de Investigación Clínica en Litiasis Urinaria, Hospital Tenon, Paris, France; Servicio de Urología, Asistencia Pública Hospitales de París, Hospital Tenon, Universidad de La Sorbona, Paris, France
| | - C Solano
- Grupo de Investigación Clínica en Litiasis Urinaria, Hospital Tenon, Paris, France; Universidad de La Sorbona, París, Francia; Servicio de Endourología, Uroclin SAS Medellín, Colombia
| | - L Candela
- Grupo de Investigación Clínica en Litiasis Urinaria, Hospital Tenon, Paris, France; Servicio de Urología, Asistencia Pública Hospitales de París, Hospital Tenon, Universidad de La Sorbona, Paris, France; Divisiónde Oncología Experimental, Unidad de Urología, URI. IRCCS Hospital San Raffaele, Universidad Vita-Salute San Raffaele, Milán, Italy
| | - M Corrales
- Grupo de Investigación Clínica en Litiasis Urinaria, Hospital Tenon, Paris, France; Servicio de Urología, Asistencia Pública Hospitales de París, Hospital Tenon, Universidad de La Sorbona, Paris, France
| | - M Chicaud
- Grupo de Investigación Clínica en Litiasis Urinaria, Hospital Tenon, Paris, France; Servicio de Urología, Asistencia Pública Hospitales de París, Hospital Tenon, Universidad de La Sorbona, Paris, France; Servicio de Urología, CHU Limoges, Limoges, France
| | - O Traxer
- Grupo de Investigación Clínica en Litiasis Urinaria, Hospital Tenon, Paris, France; Servicio de Urología, Asistencia Pública Hospitales de París, Hospital Tenon, Universidad de La Sorbona, Paris, France
| | - S Hautekeete
- Servicio de Radiología, Hospital Universitario de Gante, Gante, Belgium
| | - T Tailly
- Servicio de Urología, Hospital Universitario de Gante, Gante, Belgium
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13
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Schmidt S. AI-based approaches in the daily practice of abdominal imaging. Eur Radiol 2024; 34:495-497. [PMID: 37555958 PMCID: PMC10791913 DOI: 10.1007/s00330-023-10116-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 07/12/2023] [Accepted: 07/29/2023] [Indexed: 08/10/2023]
Affiliation(s)
- Sabine Schmidt
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Bugnon 46, 1011, Lausanne, Switzerland.
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Mukherjee P, Lee S, Elton DC, Pickhardt PJ, Summers RM. Longitudinal follow-up of incidental renal calculi on computed tomography. Abdom Radiol (NY) 2024; 49:173-181. [PMID: 37906271 DOI: 10.1007/s00261-023-04075-w] [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: 07/24/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 11/02/2023]
Abstract
RATIONALE AND OBJECTIVES Measuring small kidney stones on CT is a time-consuming task often neglected. Volumetric assessment provides a better measure of size than linear dimensions. Our objective is to analyze the growth rate and prognosis of incidental kidney stones in asymptomatic patients on CT. MATERIALS AND METHODS This retrospective study included 4266 scans from 2030 asymptomatic patients who underwent two or more nonenhanced CT scans for colorectal screening between 2004 and 2016. The DL software identified and measured the volume, location, and attenuation of 883 stones. The corresponding scans were manually evaluated, and patients without follow-up were excluded. At each follow-up, the stones were categorized as new, growing, persistent, or resolved. Stone size (volume and diameter), attenuation, and location were correlated with the outcome and growth rates of the stones. RESULTS The stone cohort comprised 407 scans from 189 (M: 124, F: 65, median age: 55.4 years) patients. The median number of stones per scan was 1 (IQR: [1, 2]). The median stone volume was 17.1 mm3 (IQR: [7.4, 43.6]) and the median peak attenuation was 308 HU (IQR: [204, 532]. The 189 initial scans contained 291stones; 91 (31.3%) resolved, 142 (48.8%) grew, and 58 (19.9) remained persistent at the first follow-up. At the second follow-up (for 27 patients with 2 follow-ups), 14/44 (31.8%) stones had resolved, 19/44 (43.2%) grew and 11/44 (25%) were persistent. The median growth rate of growing stones was 3.3 mm3/year, IQR: [1.4,7.4]. Size and attenuation had a moderate correlation (Spearman rho 0.53, P < .001 for volume, and 0.50 P < .001 for peak attenuation) with the growth rate. Growing and persistent stones had significantly greater maximum axial diameter (2.7 vs 2.3 mm, P =.047) and peak attenuation (300 vs 258 HU, P =.031) CONCLUSION: We report a 12.7% prevalence of incidental kidney stones in asymptomatic adults, of which about half grew during follow-up with a median growth rate of about 3.3 mm3/year.
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Affiliation(s)
- Pritam Mukherjee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD, 20892-1182, USA
| | - Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD, 20892-1182, USA
| | - Daniel C Elton
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD, 20892-1182, USA
| | - Perry J Pickhardt
- Department of Radiology, School of Medicine & Public Health, The University of Wisconsin, Madison, WI, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD, 20892-1182, USA.
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15
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Sabuncu Ö, Bilgehan B, Kneebone E, Mirzaei O. Effective deep learning classification for kidney stone using axial computed tomography (CT) images. BIOMED ENG-BIOMED TE 2023; 68:481-491. [PMID: 37129960 DOI: 10.1515/bmt-2022-0142] [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: 04/08/2022] [Accepted: 04/11/2023] [Indexed: 05/03/2023]
Abstract
INTRODUCTION Stone formation in the kidneys is a common disease, and the high rate of recurrence and morbidity of the disease worries all patients with kidney stones. There are many imaging options for diagnosing and managing kidney stone disease, and CT imaging is the preferred method. OBJECTIVES Radiologists need to manually analyse large numbers of CT slices to diagnose kidney stones, and this process is laborious and time-consuming. This study used deep automated learning (DL) algorithms to analyse kidney stones. The primary purpose of this study is to classify kidney stones accurately from CT scans using deep learning algorithms. METHODS The Inception-V3 model was selected as a reference in this study. Pre-trained with other CNN architectures were applied to a recorded dataset of abdominal CT scans of patients with kidney stones labelled by a radiologist. The minibatch size has been modified to 7, and the initial learning rate was 0.0085. RESULTS The performance of the eight models has been analysed with 8209 CT images recorded at the hospital for the first time. The training and test phases were processed with limited authentic recorded CT images. The outcome result of the test shows that the Inception-V3 model has a test accuracy of 98.52 % using CT images in detecting kidney stones. CONCLUSIONS The observation is that the Inception-V3 model is successful in detecting kidney stones of small size. The performance of the Inception-V3 Model is at a high level and can be used for clinical applications. The research helps the radiologist identify kidney stones with less computational cost and disregards the need for many experts for such applications.
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Affiliation(s)
- Özlem Sabuncu
- Department of Electrical and Electronic Engineering, Near East University, Nicosia, Mersin, Türkiye
| | - Bülent Bilgehan
- Department of Electrical and Electronic Engineering, Near East University, Nicosia, Mersin, Türkiye
| | - Enver Kneebone
- Vocational School of Health Services, European University of Lefke, Lefke, Mersin, Türkiye
| | - Omid Mirzaei
- Department of Biomedical Engineering, Near East University, Nicosia, Mersin, Türkiye
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Peyrottes A, Chicaud M, Fourniol C, Doizi S, Timsit MO, Méjean A, Yonneau L, Lebret T, Audenet F, Traxer O, Panthier F. Clinical Reproducibility of the Stone Volume Measurement: A "Kidney Stone Calculator" Study. J Clin Med 2023; 12:6274. [PMID: 37834918 PMCID: PMC10573675 DOI: 10.3390/jcm12196274] [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: 09/05/2023] [Revised: 09/25/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND An accurate estimation of the stone burden is the key factor for predicting retrograde intra-renal surgical outcomes. Volumetric calculations better stratify stone burden than linear measurements. We developed a free software to assess the stone volume and estimate the lithotrity duration according to 3D-segmented stone volumes, namely the Kidney Stone Calculator (KSC). The present study aimed to validate the KSC's reproducibility in clinical cases evaluating its inter-observer and intra-observer correlations. METHODS Fifty patients that harbored renal stones were retrospectively selected from a prospective cohort. For each patient, three urologists with different experience levels in stone management made five measurements of the stone volume on non-contrast-enhanced computed tomography (NCCT) images using the KSC. RESULTS the overall inter-observer correlation (Kendall's concordance coefficient) was 0.99 (p < 0.0001). All three paired analyses of the inter-observer reproducibility were superior to 0.8. The intra-observer variation coefficients varied from 4% to 6%, and Kendall's intra-observer concordance coefficient was found to be superior to 0.98 (p < 0.0001) for each participant. Subgroup analyses showed that the segmentation of complex stones seems to be less reproductible. CONCLUSIONS The Kidney Stone Calculator is a reliable tool for the stone burden estimation. Its extension for calculating the lithotrity duration is of major interest and could help the practitioner in surgical planning.
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Affiliation(s)
- Arthur Peyrottes
- GRC n°20, Groupe de Recherche Clinique Sur La Lithiase Urinaire, Hôpital Tenon, Sorbonne Université, 75020 Paris, France; (A.P.); (M.C.); (S.D.); (O.T.)
- Service D’Urologie, Hôpital Européen Georges Pompidou, AP-HP.Centre, Université Paris-Cité, 20 rue Leblanc, 75015 Paris, France; (C.F.); (M.-O.T.); (A.M.); (F.A.)
| | - Marie Chicaud
- GRC n°20, Groupe de Recherche Clinique Sur La Lithiase Urinaire, Hôpital Tenon, Sorbonne Université, 75020 Paris, France; (A.P.); (M.C.); (S.D.); (O.T.)
- Service D’Urologie, Hôpital Tenon, AP-HP, Sorbonne Université, 4 rue de la Chine, 75020 Paris, France
- PIMM Laboratory, UMR 8006 CNRS-Arts Et Métiers ParisTech, 151 bd de l’Hôpital, 75013 Paris, France
- Service d’Urologie, CHU de Limoges, 2 Avenue Martin Luther King, 87000 Limoges, France
| | - Cyril Fourniol
- Service D’Urologie, Hôpital Européen Georges Pompidou, AP-HP.Centre, Université Paris-Cité, 20 rue Leblanc, 75015 Paris, France; (C.F.); (M.-O.T.); (A.M.); (F.A.)
| | - Steeve Doizi
- GRC n°20, Groupe de Recherche Clinique Sur La Lithiase Urinaire, Hôpital Tenon, Sorbonne Université, 75020 Paris, France; (A.P.); (M.C.); (S.D.); (O.T.)
- Service D’Urologie, Hôpital Tenon, AP-HP, Sorbonne Université, 4 rue de la Chine, 75020 Paris, France
- PIMM Laboratory, UMR 8006 CNRS-Arts Et Métiers ParisTech, 151 bd de l’Hôpital, 75013 Paris, France
| | - Marc-Olivier Timsit
- Service D’Urologie, Hôpital Européen Georges Pompidou, AP-HP.Centre, Université Paris-Cité, 20 rue Leblanc, 75015 Paris, France; (C.F.); (M.-O.T.); (A.M.); (F.A.)
| | - Arnaud Méjean
- Service D’Urologie, Hôpital Européen Georges Pompidou, AP-HP.Centre, Université Paris-Cité, 20 rue Leblanc, 75015 Paris, France; (C.F.); (M.-O.T.); (A.M.); (F.A.)
| | - Laurent Yonneau
- Service d’Urologie, Hôpital Foch-Université Paris Saclay-UVSQ, 40 rue Worth, 92150 Suresnes, France; (L.Y.); (T.L.)
| | - Thierry Lebret
- Service d’Urologie, Hôpital Foch-Université Paris Saclay-UVSQ, 40 rue Worth, 92150 Suresnes, France; (L.Y.); (T.L.)
| | - François Audenet
- Service D’Urologie, Hôpital Européen Georges Pompidou, AP-HP.Centre, Université Paris-Cité, 20 rue Leblanc, 75015 Paris, France; (C.F.); (M.-O.T.); (A.M.); (F.A.)
| | - Olivier Traxer
- GRC n°20, Groupe de Recherche Clinique Sur La Lithiase Urinaire, Hôpital Tenon, Sorbonne Université, 75020 Paris, France; (A.P.); (M.C.); (S.D.); (O.T.)
- Service D’Urologie, Hôpital Tenon, AP-HP, Sorbonne Université, 4 rue de la Chine, 75020 Paris, France
- PIMM Laboratory, UMR 8006 CNRS-Arts Et Métiers ParisTech, 151 bd de l’Hôpital, 75013 Paris, France
| | - Frederic Panthier
- GRC n°20, Groupe de Recherche Clinique Sur La Lithiase Urinaire, Hôpital Tenon, Sorbonne Université, 75020 Paris, France; (A.P.); (M.C.); (S.D.); (O.T.)
- Service D’Urologie, Hôpital Européen Georges Pompidou, AP-HP.Centre, Université Paris-Cité, 20 rue Leblanc, 75015 Paris, France; (C.F.); (M.-O.T.); (A.M.); (F.A.)
- Service D’Urologie, Hôpital Tenon, AP-HP, Sorbonne Université, 4 rue de la Chine, 75020 Paris, France
- PIMM Laboratory, UMR 8006 CNRS-Arts Et Métiers ParisTech, 151 bd de l’Hôpital, 75013 Paris, France
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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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18
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Mukherjee P, Lee S, Elton DC, Nakada SY, Pickhardt PJ, Summers RM. Fully Automated Longitudinal Assessment of Renal Stone Burden on Serial CT Imaging Using Deep Learning. J Endourol 2023; 37:948-955. [PMID: 37310890 PMCID: PMC10387157 DOI: 10.1089/end.2023.0066] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023] Open
Abstract
Purpose: Use deep learning (DL) to automate the measurement and tracking of kidney stone burden over serial CT scans. Materials and Methods: This retrospective study included 259 scans from 113 symptomatic patients being treated for urolithiasis at a single medical center between 2006 and 2019. These patients underwent a standard low-dose noncontrast CT scan followed by ultra-low-dose CT scans limited to the level of the kidneys. A DL model was used to detect, segment, and measure the volume of all stones in both initial and follow-up scans. The stone burden was characterized by the total volume of all stones in a scan (SV). The absolute and relative change of SV, (SVA and SVR, respectively) over serial scans were computed. The automated assessments were compared with manual assessments using concordance correlation coefficient (CCC), and their agreement was visualized using Bland-Altman and scatter plots. Results: Two hundred twenty-eight out of 233 scans with stones were identified by the automated pipeline; per-scan sensitivity was 97.8% (95% confidence interval [CI]: 96.0-99.7). The per-scan positive predictive value was 96.6% (95% CI: 94.4-98.8). The median SV, SVA, and SVR were 476.5 mm3, -10 mm3, and 0.89, respectively. After removing outliers outside the 5th and 95th percentiles, the CCC measuring agreement on SV, SVA, and SVR were 0.995 (0.992-0.996), 0.980 (0.972-0.986), and 0.915 (0.881-0.939), respectively Conclusions: The automated DL-based measurements showed good agreement with the manual assessments of the stone burden and its interval change on serial CT scans.
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Affiliation(s)
- Pritam Mukherjee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland, USA
| | - Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland, USA
| | - Daniel C. Elton
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland, USA
| | - Stephen Y. Nakada
- Department of Radiology, The University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Perry J. Pickhardt
- Department of Radiology, The University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ronald M. Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland, USA
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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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20
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Mahmud S, Abbas TO, Mushtak A, Prithula J, Chowdhury MEH. Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata. Cancers (Basel) 2023; 15:3189. [PMID: 37370799 DOI: 10.3390/cancers15123189] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/30/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
Kidney cancers are one of the most common malignancies worldwide. Accurate diagnosis is a critical step in the management of kidney cancer patients and is influenced by multiple factors including tumor size or volume, cancer types and stages, etc. For malignant tumors, partial or radical surgery of the kidney might be required, but for clinicians, the basis for making this decision is often unclear. Partial nephrectomy could result in patient death due to cancer if kidney removal was necessary, whereas radical nephrectomy in less severe cases could resign patients to lifelong dialysis or need for future transplantation without sufficient cause. Using machine learning to consider clinical data alongside computed tomography images could potentially help resolve some of these surgical ambiguities, by enabling a more robust classification of kidney cancers and selection of optimal surgical approaches. In this study, we used the publicly available KiTS dataset of contrast-enhanced CT images and corresponding patient metadata to differentiate four major classes of kidney cancer: clear cell (ccRCC), chromophobe (chRCC), papillary (pRCC) renal cell carcinoma, and oncocytoma (ONC). We rationalized these data to overcome the high field of view (FoV), extract tumor regions of interest (ROIs), classify patients using deep machine-learning models, and extract/post-process CT image features for combination with clinical data. Regardless of marked data imbalance, our combined approach achieved a high level of performance (85.66% accuracy, 84.18% precision, 85.66% recall, and 84.92% F1-score). When selecting surgical procedures for malignant tumors (RCC), our method proved even more reliable (90.63% accuracy, 90.83% precision, 90.61% recall, and 90.50% F1-score). Using feature ranking, we confirmed that tumor volume and cancer stage are the most relevant clinical features for predicting surgical procedures. Once fully mature, the approach we propose could be used to assist surgeons in performing nephrectomies by guiding the choices of optimal procedures in individual patients with kidney cancer.
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Affiliation(s)
- Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Tariq O Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha 26999, Qatar
- Department of Surgery, Weill Cornell Medicine-Qatar, Doha 24811, Qatar
- College of Medicine, Qatar University, Doha 2713, Qatar
| | - Adam Mushtak
- Clinical Imaging Department, Hamad Medical Corporation, Doha 3050, Qatar
| | - Johayra Prithula
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh
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21
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Pickhardt PJ, Summers RM, Garrett JW, Krishnaraj A, Agarwal S, Dreyer KJ, Nicola GN. Opportunistic Screening: Radiology Scientific Expert Panel. Radiology 2023; 307:e222044. [PMID: 37219444 PMCID: PMC10315516 DOI: 10.1148/radiol.222044] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/03/2022] [Accepted: 12/01/2022] [Indexed: 05/24/2023]
Abstract
Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematically leveraging these incidental imaging findings. Although opportunistic screening can apply to imaging modalities such as conventional radiography, US, and MRI, most attention to date has focused on body CT by using artificial intelligence (AI)-assisted methods. Body CT represents an ideal high-volume modality whereby a quantitative assessment of tissue composition (eg, bone, muscle, fat, and vascular calcium) can provide valuable risk stratification and help detect unsuspected presymptomatic disease. The emergence of "explainable" AI algorithms that fully automate these measurements could eventually lead to their routine clinical use. Potential barriers to widespread implementation of opportunistic CT screening include the need for buy-in from radiologists, referring providers, and patients. Standardization of acquiring and reporting measures is needed, in addition to expanded normative data according to age, sex, and race and ethnicity. Regulatory and reimbursement hurdles are not insurmountable but pose substantial challenges to commercialization and clinical use. Through demonstration of improved population health outcomes and cost-effectiveness, these opportunistic CT-based measures should be attractive to both payers and health care systems as value-based reimbursement models mature. If highly successful, opportunistic screening could eventually justify a practice of standalone "intended" CT screening.
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Affiliation(s)
- Perry J. Pickhardt
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Ronald M. Summers
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - John W. Garrett
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Arun Krishnaraj
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Sheela Agarwal
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Keith J. Dreyer
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Gregory N. Nicola
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
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22
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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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23
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Wang P, Cai S, Tan W, Yan B, Zhong Y. ClusterNet: a clustering distributed prior embedded detection network for early-stage esophageal squamous cell carcinoma diagnosis. Med Phys 2023; 50:854-866. [PMID: 36222486 DOI: 10.1002/mp.16041] [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: 03/21/2022] [Revised: 08/16/2022] [Accepted: 09/19/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Early and accurate diagnosis of esophageal squamous cell carcinoma (ESCC) is important for reducing mortality. Analyzing intrapapillary capillary loops' (IPCLs) patterns on magnification endoscopy with narrow band imaging (ME-NBI) has been demonstrated effective in the diagnosis of early-stage ESCC. However, even experienced endoscopists may face difficulty in finding and classifying countless IPCLs on ME-NBI. PURPOSE We propose a novel clustering prior embedded detection network: ClusterNet. ClusterNet is capable of analyzing the distribution of IPCLs on ME-NBI automatically and enables endoscopists to overview multiple types of visualization. With ClusterNet assisting, endoscopists may observe ME-NBI images more efficiently, thus they may also predict the pathology and make medical decisions more easily. METHODS We propose the first large-scale ME-NBI dataset with fine-grained annotations by consensus of expert endoscopists. The dataset is splitted into a training set and an independent testing set based on patients. With two strategies for embedding, ClusterNet can automatically take the clustering effect into consideration. Prior to this work, none of the existing approaches take the clustering effect, which is rather important in classifying the IPCLs, into account. RESULTS ClusterNet achieves an average precision of 81.2% and an average recall of 90.0% for the detection of IPCLs patterns on each patient of the independent testing set. We also compare ClusterNet with other state-of-the-art detection approaches. The performance of ClusterNet with embedding strategies is consistently superior to that of other approaches in terms of average precision, recall and F2-Score. CONCLUSIONS Experiments demonstrate that our proposed method is able to detect almost all the IPCLs patterns on ME-NBI and classify them according to the Japanese Endoscopic Society (JES) classification accurately.
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Affiliation(s)
- Peisheng Wang
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Shilun Cai
- Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weimin Tan
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Bo Yan
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Yunshi Zhong
- Zhongshan Hospital, Fudan University, Shanghai, China
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24
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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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25
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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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Mukherjee P, Lee S, Pickhardt PJ, Summers RM. Automated Assessment of Renal Calculi in Serial Computed Tomography Scans. APPLICATIONS OF MEDICAL ARTIFICIAL INTELLIGENCE : FIRST INTERNATIONAL WORKSHOP, AMAI 2022, HELD IN CONJUNCTION WITH MICCAI 2022, SINGAPORE, SEPTEMBER 18, 2022, PROCEEDINGS. AMAI (WORKSHOP) (1ST : 2022 : SINGAPORE ; ONLINE) 2022; 13540:39-48. [PMID: 37093905 PMCID: PMC10115460 DOI: 10.1007/978-3-031-17721-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
An automated pipeline is developed for the serial assessment of renal calculi using computed tomography (CT) scans obtained at multiple time points. This retrospective study included 722 scans from 330 patients chosen from 8544 asymptomatic patients who underwent two or more CTC (CT colonography) or non-enhanced abdominal CT scans between 2004 and 2016 at a single medical center. A pre-trained deep learning (DL) model was used to segment the kidneys and the calculi on the CT scans at each time point. Based on the output of the DL, 330 patients were identified as having a stone candidate on at least one time point. Then, for every patient in this group, the kidneys from different time points were registered to each other, and the calculi present at multiple time points were matched to each other using proximity on the registered scans. The automated pipeline was validated by having a blinded radiologist assess the changes manually. New graph-based metrics are introduced in order to evaluate the performance of our pipeline. Our method shows high fidelity in tracking changes in renal calculi over multiple time points.
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Affiliation(s)
- Pritam Mukherjee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Perry J Pickhardt
- Department of Radiology, The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
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Danilovic A. Deep learning is a promising technology and seems to be the future of the CT stone evaluation. Int Braz J Urol 2022; 48:840-841. [PMID: 35838510 PMCID: PMC9388183 DOI: 10.1590/s1677-5538.ibju.2022.0132.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Alexandre Danilovic
- Departamento de Urologia, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo - FMUSP, São Paulo, SP, Brasil.,Hospital Alemão Oswaldo Cruz, São Paulo, SP, Brasil
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Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images. Diagnostics (Basel) 2022; 12:diagnostics12081788. [PMID: 35892498 PMCID: PMC9330428 DOI: 10.3390/diagnostics12081788] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/20/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022] Open
Abstract
Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) images have been limited. Radiomics and machine learning analyses of renal diseases rely on the automatic segmentation of kidneys in CT images. Inspired by this, our primary aim is to utilize deep semantic segmentation learning models with a proposed training scheme to achieve precise and accurate segmentation outcomes. Moreover, this work aims to provide the community with an open-source, unenhanced abdominal CT dataset for training and testing the deep learning segmentation networks to segment kidneys and detect kidney stones. Five variations of deep segmentation networks are trained and tested both dependently (based on the proposed training scheme) and independently. Upon comparison, the models trained with the proposed training scheme enable the highly accurate 2D and 3D segmentation of kidneys and kidney stones. We believe this work is a fundamental step toward AI-driven diagnostic strategies, which can be an essential component of personalized patient care and improved decision-making in treating kidney diseases.
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