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Kim J, Kwak CW, Uhmn S, Lee J, Yoo S, Cho MC, Son H, Jeong H, Choo MS. A Novel Deep Learning-based Artificial Intelligence System for Interpreting Urolithiasis in Computed Tomography. Eur Urol Focus 2024:S2405-4569(24)00123-8. [PMID: 38997836 DOI: 10.1016/j.euf.2024.07.003] [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: 03/06/2024] [Revised: 06/05/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024]
Abstract
BACKGROUND AND OBJECTIVE Our aim was to develop an artificial intelligence (AI) system for detection of urolithiasis in computed tomography (CT) images using advanced deep learning capable of real-time calculation of stone parameters such as volume and density, which are essential for treatment decisions. The performance of the system was compared to that of urologists in emergency room (ER) scenarios. METHODS Axial CT images for patients who underwent stone surgery between August 2022 and July 2023 comprised the data set, which was divided into 70% for training, 10% for internal validation, and 20% for testing. Two urologists and an AI specialist annotated stones using Labelimg for ground-truth data. The YOLOv4 architecture was used for training, with acceleration via an RTX 4900 graphics processing unit (GPU). External validation was performed using CT images for 100 patients with suspected urolithiasis. KEY FINDINGS AND LIMITATIONS The AI system was trained on 39 433 CT images, of which 9.1% were positive. The system achieved accuracy of 95%, peaking with a 1:2 positive-to-negative sample ratio. In a validation set of 5736 images (482 positive), accuracy remained at 95%. Misses (2.6%) were mainly irregular stones. False positives (3.4%) were often due to artifacts or calcifications. External validation using 100 CT images from the ER revealed accuracy of 94%; cases that were missed were mostly ureterovesical junction stones, which were not included in the training set. The AI system surpassed human specialists in speed, analyzing 150 CT images in 13 s, versus 38.6 s for evaluation by urologists and 23 h for formal reading. The AI system calculated stone volume in 0.2 s, versus 77 s for calculation by urologists. CONCLUSIONS AND CLINICAL IMPLICATIONS Our AI system, which uses advanced deep learning, assists in diagnosing urolithiasis with 94% accuracy in real clinical settings and has potential for rapid diagnosis using standard consumer-grade GPUs. PATIENT SUMMARY We developed a new AI (artificial intelligence) system that can quickly and accurately detect kidney stones in CT (computed tomography) scans. Testing showed that this system is highly effective, with accuracy of 94% for real cases in the emergency department. It is much faster than traditional methods and provides rapid and reliable results to help doctors in making better treatment decisions for their patients.
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Affiliation(s)
- Jin Kim
- Department of Computer Engineering, Hallym University, Chuncheon, South Korea
| | - Chan Woo Kwak
- Land Combat R&D Center, Hanwha Systems, Gumi, South Korea
| | - Saangyong Uhmn
- Department of Computer Engineering, Hallym University, Chuncheon, South Korea
| | - Junghoon Lee
- Department of Urology, Boramae Medical Center, Seoul Metropolitan Government-Seoul National University, Seoul, South Korea
| | - Sangjun Yoo
- Department of Urology, Boramae Medical Center, Seoul Metropolitan Government-Seoul National University, Seoul, South Korea
| | - Min Chul Cho
- Department of Urology, Boramae Medical Center, Seoul Metropolitan Government-Seoul National University, Seoul, South Korea
| | - Hwancheol Son
- Department of Urology, Boramae Medical Center, Seoul Metropolitan Government-Seoul National University, Seoul, South Korea
| | - Hyeon Jeong
- Department of Urology, Boramae Medical Center, Seoul Metropolitan Government-Seoul National University, Seoul, South Korea
| | - Min Soo Choo
- Department of Urology, Boramae Medical Center, Seoul Metropolitan Government-Seoul National University, Seoul, South Korea.
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Wang M, Zhang Y, Tong H, Liu B, Chen J, Ma Q, Zhang Y. Comparison of ureteral stone measurements for predicting the efficacy of a single session of extracorporeal shockwave lithotripsy: one-, two-, and three-dimensional computed tomography measurements. Urolithiasis 2024; 52:43. [PMID: 38441706 DOI: 10.1007/s00240-024-01538-7] [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: 08/19/2023] [Accepted: 01/28/2024] [Indexed: 03/07/2024]
Abstract
The objective of this study was to compare the value of one-, two- and three-dimensional computed tomography (CT) measurements for predicting the efficacy of a single session of extracorporeal shock wave lithotripsy (ESWL) in patients with a single ureteral stone. A total of 165 patients were included based on the inclusion and exclusion criteria. Different models were constructed using a combination of patients' clinical data and measurements obtained by manual sketching and automated extraction software. Multivariate logistic regression was used to develop the models. Receiver operating characteristic curves were used to assess the performance of the models. There was good interobserver agreement for all measurements in different dimensions (P < 0.001). We also found that hydronephrosis, the largest diameter, the largest area, volume, and mean CT value were significantly greater in the failure group than in the success group (P < 0.01). Furthermore, all sizes and CT measurement values were found to be independent predictors for predicting efficacy after one session of ESWL (P < 0.05). In addition, the multivariate logistic analysis showed that the area under the curve (AUC) for two-dimensional and three-dimensional measurements was superior to that of one-dimensional measurement (P < 0.01). However, when size alone was included as a measurable predictor, there was no significant difference in the AUC among the one-, two-, and three-dimensional measurements (P > 0.05). In summary, after adjusting for clinical data, two- and three-dimensional measurements combining ureteral stone size and CT values were found to be the best predictors of ESWL efficacy, and software-based three-dimensional measurements should be considered to avoid interobserver variability in clinical practice.
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Affiliation(s)
- Meng Wang
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, China.
| | - Yueyue Zhang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, China
| | - Hua Tong
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, China
| | - Bin Liu
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, China
| | - Jueqi Chen
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, China
| | - Qi Ma
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, China
| | - Yingchun Zhang
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, 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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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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Babajide R, Lembrikova K, Ziemba J, Ding J, Li Y, Fermin AS, Fan Y, Tasian GE. Automated Machine Learning Segmentation and Measurement of Urinary Stones on CT Scan. Urology 2022; 169:41-46. [PMID: 35908740 PMCID: PMC9936246 DOI: 10.1016/j.urology.2022.07.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 07/06/2022] [Accepted: 07/17/2022] [Indexed: 10/16/2022]
Abstract
OBJECTIVES To evaluate the performance of an engineered machine learning algorithm to identify kidney stones and measure stone characteristics without the need for human input. METHODS We performed a cross-sectional study of 94 children and adults who had kidney stones identified on non-contrast CT. A previously developed deep learning algorithm was trained to segment renal anatomy and kidney stones and to measure stone features. The performance and speed of the algorithm to measure renal anatomy and kidney stone features were compared to the current gold standard of human measurement performed by 3 independent reviewers. RESULTS The algorithm was 100% sensitive and 100% specific in detecting individual kidney stones. The mean stone volume segmented by the algorithm was smaller than that of human reviewers and had moderate overlap (Dice score: 0.66). There was substantial variation between human reviewers in total segmented stone volume (Jaccard score: 0.17) and volume of the single largest stone (Jaccard score: 0.33). Stone segmentations performed by the machine learning algorithm more precisely approximated stone borders than those performed by human reviewers on qualitative assessment. CONCLUSION An engineered machine learning algorithm can identify and characterize stones more accurately and reliably than humans, which has the potential to improve the precision and efficiency of assessing kidney stone burden.
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Affiliation(s)
- Rilwan Babajide
- University of Chicago Pritzker School of Medicine, Chicago, IL
| | | | - Justin Ziemba
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; Department of Surgery, Division of Urology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - James Ding
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Yuemeng Li
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; The Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Antoine Selman Fermin
- Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA
| | - Yong Fan
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; The Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Gregory E Tasian
- Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biostatistics, Epidemiology, and Informatics; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
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Vuruskan E, Dilek O, Karkin K, Unal U, Ayhan L, Sener NC. Volume should be used instead of diameter for kidney stones between 10 and 20 mm to determine the type of surgery and increase success. Urolithiasis 2022; 50:215-221. [PMID: 35075495 DOI: 10.1007/s00240-022-01305-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/14/2022] [Indexed: 11/24/2022]
Abstract
Aim of this study is to categorize stones between 10 and 20 mm according to stone diameter or volume and compare mini percutaneous nephrolithotomy (mPNL) and retrograde intrarenal surgery (RIRS) outcomes. Files of 515 patients who underwent surgery for kidney stones with sizes 10-20 mm were reviewed. Patients were divided into RIRS or mPNL groups. An attempt was made to find the diameter and volume threshold values above which the success of the operation, complication rates and the number of auxiliary treatments deteriorated. Subgroup analysis was performed below and above the threshold value to reveal the optimal treatment methods. RIRS complications increased with volumes above 1064 mm3, number of auxiliary interventions increased with volumes above 1256 mm3, and success of the operation decreased with volumes above 1416 mm3. A subgroup analysis under and over 1064 mm3 was performed in RIRS group. Complication and auxiliary treatment rates were higher, operative success was lower in patients with a stone volume greater than 1064 mm3. In patients who underwent RIRS, for every 1000 mm3 increase in stone volume success of the operation decreased by 2.1 times, while the probability of auxiliary treatment increased by 2.8 times. In patients with kidney stones between 10 and 20 mm, it is more meaningful to use volume instead of diameter to determine the success rate. When mPNL is used instead of RIRS for volumes greater than 1064 mm3, the success rate will be higher, complication rate will be similar, and the need for auxiliary treatment will be lower.
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Affiliation(s)
- Ediz Vuruskan
- Department of Urology, Health Sciences University, Adana City Training and Research Hospital, Dr. Mithat Özsan Bulvarı Kışla Mah. 4522 Sok. No:1 Yuregir, Adana, Turkey.
| | - Okan Dilek
- Department of Radiology, Health Sciences University, Adana City Training and Research Hospital, Adana, Turkey
| | - Kadir Karkin
- Department of Urology, Health Sciences University, Adana City Training and Research Hospital, Dr. Mithat Özsan Bulvarı Kışla Mah. 4522 Sok. No:1 Yuregir, Adana, Turkey
| | - Umut Unal
- Department of Urology, Health Sciences University, Adana City Training and Research Hospital, Dr. Mithat Özsan Bulvarı Kışla Mah. 4522 Sok. No:1 Yuregir, Adana, Turkey
| | - Lokman Ayhan
- Department of Urology, Health Sciences University, Adana City Training and Research Hospital, Dr. Mithat Özsan Bulvarı Kışla Mah. 4522 Sok. No:1 Yuregir, Adana, Turkey
| | - Nevzat Can Sener
- Department of Urology, Health Sciences University, Adana City Training and Research Hospital, Dr. Mithat Özsan Bulvarı Kışla Mah. 4522 Sok. No:1 Yuregir, Adana, Turkey
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Abstract
PURPOSE OF REVIEW Radiological imaging techniques are a fast developing field in medicine. Therefore, the purpose of this review was to identify and discuss the latest changes of modern imaging techniques in the management of urinary stone disease. RECENT FINDINGS The introduction of iterative image reconstruction enables low-dose and ultra-low-dose (ULD) protocols. Although current guidelines recommend their utilization in nonobese patients recent studies indicate that low-dose imaging may be feasible in obese (<30 kg/m) but not in bariatric patients. Use of dual energy computed tomography (CT) technologies should balance between additional information and radiation dose aspects. If available on a dose neutral basis, dual energy imaging and analysis should be performed. Current guidelines recommend measuring the largest diameter for clinical decision making; however, recent studies suggest a benefit from measuring the volume based on multiplanar reformation. Quantitative imaging is still an experimental approach. SUMMARY The use of low-dose and even ULD CT protocols should be diagnostic standard, even in obese patients. If dual energy imaging is available, it should be limited to specific clinical questions. The stone volume should be reported in addition to the largest diameter for treatment decision and a more valid comparability of upcoming studies.
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Optimal non-invasive treatment of 1–2.5 cm radiolucent renal stones: oral dissolution therapy, shock wave lithotripsy or combined treatment—a randomized controlled trial. World J Urol 2019; 38:207-212. [DOI: 10.1007/s00345-019-02746-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 03/27/2019] [Indexed: 12/18/2022] Open
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Gómez-Núñez JG. Editorial Comment on: Automated Computer Software Compared with Manual Measurements for CT-Based Urinary Stone Metrics: An Evaluation Study by Bell et al.. J Endourol 2018; 32:462-463. [DOI: 10.1089/end.2018.0207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Joel Gustavo Gómez-Núñez
- Departamento de Urología, Centro Universitario de Ciencias de la Salud (CUCS), Universidad de Guadalajara, Guadalajara Jal, México
- Urólogo/Endourólogo, Hospital General Regional (HGR) 180, Instituto Mexicano del Seguro Social (IMSS) Delegación Jalisco, Tlajomulco de Zúñiga Jal, México
- Departamento de Urología, Urólogo/Endourólogo, Instituto Jalisciense de Cancerología, Secretaria de Salud, Guadalajara Jal, Mexico
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