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Nedbal C, Cerrato C, Jahrreiss V, Pietropaolo A, Galosi AB, Castellani D, Somani BK. Trends of "Artificial Intelligence, Machine Learning, Virtual Reality, and Radiomics in Urolithiasis" over the Last 30 Years (1994-2023) as Published in the Literature (PubMed): A Comprehensive Review. J Endourol 2024; 38:788-798. [PMID: 37885228 DOI: 10.1089/end.2023.0263] [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: 10/28/2023] Open
Abstract
Purpose: To analyze the bibliometric publication trend on the application of "Artificial Intelligence (AI) and its subsets (Machine Learning-ML, Virtual reality-VR, Radiomics) in Urolithiasis" over 3 decades. We looked at the publication trends associated with AI and stone disease, including both clinical and surgical applications, and training in endourology. Methods: Through a MeshTerms research on PubMed, we performed a comprehensive review from 1994-2023 for all published articles on "AI, ML, VR, and Radiomics." Articles were then divided into three categories as follows: A-Clinical (Nonsurgical), B-Clinical (Surgical), and C-Training articles, and articles were then assigned to following three periods: Period-1 (1994-2003), Period-2 (2004-2013), and Period-3 (2014-2023). Results: A total of 343 articles were noted (Groups A-129, B-163, and C-51), and trends increased from Period-1 to Period-2 at 123% (p = 0.009) and to period-3 at 453% (p = 0.003). This increase from Period-2 to Period-3 for groups A, B, and C was 476% (p = 0.019), 616% (0.001), and 185% (p < 0.001), respectively. Group A articles included rise in articles on "stone characteristics" (+2100%; p = 0.011), "renal function" (p = 0.002), "stone diagnosis" (+192%), "prediction of stone passage" (+400%), and "quality of life" (+1000%). Group B articles included rise in articles on "URS" (+2650%, p = 0.008), "PCNL"(+600%, p = 0.001), and "SWL" (+650%, p = 0.018). Articles on "Targeting" (+453%, p < 0.001), "Outcomes" (+850%, p = 0.013), and "Technological Innovation" (p = 0.0311) had rising trends. Group C articles included rise in articles on "PCNL" (+300%, p = 0.039) and "URS" (+188%, p = 0.003). Conclusion: Publications on AI and its subset areas for urolithiasis have seen an exponential increase over the last decade, with an increase in surgical and nonsurgical clinical areas, as well as in training. Future AI related growth in the field of endourology and urolithiasis is likely to improve training, patient centered decision-making, and clinical outcomes.
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Affiliation(s)
- Carlotta Nedbal
- Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom
- Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic University of Le Marche, Ancona, Italy
| | - Clara Cerrato
- Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom
| | - Victoria Jahrreiss
- Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Amelia Pietropaolo
- Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom
| | - Andrea Benedetto Galosi
- Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic University of Le Marche, Ancona, Italy
| | - Daniele Castellani
- Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic University of Le Marche, Ancona, Italy
| | - Bhaskar Kumar Somani
- Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom
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Campobasso D, Panizzi M, Bellini V, Ferretti S, Amparore D, Castellani D, Fiori C, Puliatti S, Pietropaolo A, Somani BK, Micali S, Porpiglia F, Maestroni UV, Bignami EG. Application of AI in urolithiasis risk of infection: a scoping review. Minerva Urol Nephrol 2024; 76:295-302. [PMID: 38920010 DOI: 10.23736/s2724-6051.24.05686-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
INTRODUCTION Artificial intelligence and machine learning are the new frontier in urology; they can assist the diagnostic work-up and in prognostication bring superior to the existing nomograms. Infectious events and in particular the septic risk, are one of the most common and in some cases life threatening complication in patients with urolithiasis. We performed a scoping review to provide an overview of the current application of AI in prediction the infectious complications in patients affected by urolithiasis. EVIDENCE ACQUISITION A systematic scoping review of the literature was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews (PRISMA-ScR) guidelines by screening Medline, PubMed, and Embase to detect pertinent studies. EVIDENCE SYNTHESIS A total of 467 articles were found, of which nine met the inclusion criteria and were considered. All studies are retrospective and published between 2021 and 2023. Only two studies performed an external validation of the described models. The main event considered is urosepsis in four articles, urinary tract infection in two articles and diagnosis of infection stones in three articles. Different AI models were trained, each of which exploited several types and numbers of variables. All studies reveal good performance. Random forest and artificial neural networks seem to have higher AUC, specificity and sensibility and perform better than the traditional statistical analysis. CONCLUSIONS Further prospective and multi-institutional studies with external validation are needed to better clarify which variables and AI models should be integrated in our clinical practice to predict infectious events.
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Affiliation(s)
| | - Matteo Panizzi
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Stefania Ferretti
- Department of Urology, University of Modena e Reggio Emilia, Modena, Italy
| | - Daniele Amparore
- Division of Urology, Department of Oncology, School of Medicine, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Daniele Castellani
- Department of Urology, Azienda Ospedaliera Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy
| | - Cristian Fiori
- Division of Urology, Department of Oncology, School of Medicine, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Stefano Puliatti
- Department of Urology, University of Modena e Reggio Emilia, Modena, Italy
| | - Amelia Pietropaolo
- Department of Urology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Bhaskar K Somani
- Department of Urology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Salvatore Micali
- Department of Urology, University of Modena e Reggio Emilia, Modena, Italy
| | - Francesco Porpiglia
- Division of Urology, Department of Oncology, School of Medicine, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | | | - Elena G Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
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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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Noble PA, Hamilton BD, Gerber G. Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients. PLoS One 2024; 19:e0301812. [PMID: 38696418 PMCID: PMC11065282 DOI: 10.1371/journal.pone.0301812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/24/2024] [Indexed: 05/04/2024] Open
Abstract
Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.
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Affiliation(s)
- Peter A. Noble
- Department of Microbiology, University of Alabama Birmingham, Birmingham, AL, United States of America
| | - Blake D. Hamilton
- School of Medicine, University of Utah, Salt Lake City, UT, United States of America
| | - Glenn Gerber
- University of Chicago Medical Center, Chicago, IL, United States of America
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Alexa R, Kranz J, Kramann R, Kuppe C, Sanyal R, Hayat S, Casas Murillo LF, Hajili T, Hoffmann M, Saar M. Harnessing Artificial Intelligence for Enhanced Renal Analysis: Automated Detection of Hydronephrosis and Precise Kidney Segmentation. EUR UROL SUPPL 2024; 62:19-25. [PMID: 38585207 PMCID: PMC10998270 DOI: 10.1016/j.euros.2024.01.017] [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] [Accepted: 01/31/2024] [Indexed: 04/09/2024] Open
Abstract
Background and objective Hydronephrosis is essential in the diagnosis of renal colic. We automated the detection of hydronephrosis from ultrasound images to standardize the therapy and reduce the misdiagnosis of renal colic. Methods Anonymously collected ultrasound images of human kidneys, both normal and hydronephrotic, were preprocessed for neural networks. Six "state of the art" models were trained and cross-validated for the detection of hydronephrosis, and two convolutional networks were used for kidney segmentation. In the testing phase, performance metrics included true positives, true negatives, false positives, false negatives, accuracy, and F1 score, while the evaluation of the segmentation task involved accuracy, precision, dice, jaccard, recall, and ASSD. Key findings and limitations A total of 523 sonographic kidney images (423 nonhydronephrotic and 100 hydronephrotic) were collected from three different ultrasound devices. After training on this dataset, all models were used to evaluate 200 new ultrasound kidney images (142 nonhydronephrotic and 58 hydronephrotic kidneys). The highest validation accuracy (98.5%) was achieved by the AlexNet model (GoogLeNet 97%, AlexNet_v2 96%, ResNet50 96%, ResNet101 97.5%, and ResNet152 95%). The deeplabv3_resnet50 and deeplabv3_resnet101 reached a dice coefficient of 94.74% and 94.48%, respectively, on the task of automated kidney segmentation. The study is limited by analyzing only hydronephrosis, but this specific focus enabled high detection accuracy. Conclusions and clinical implications We show that our automated ultrasound deep learning model can be trained and used to interpret and segmentate ultrasound images from different sources with high accuracy. This method will serve as an automated tool in the diagnostic algorithm of acute renal failure in the future. Patient summary Hydronephrosis is crucial in the diagnosis of renal colic. Recent advances in artificial intelligence allow automated detection of hydronephrosis in ultrasound images with high accuracy. These methods will help standardize the diagnosis and treatment renal colic.
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Affiliation(s)
- Radu Alexa
- Department of Urology and Pediatric Urology, University Hospital, RWTH Aachen University, Aachen, Germany
| | - Jennifer Kranz
- Department of Urology and Pediatric Urology, University Hospital, RWTH Aachen University, Aachen, Germany
- Department of Urology and Kidney Transplantation, Martin Luther University, Halle (Saale), Germany
| | - Rafael Kramann
- Department of Nephrology, Rheumatology, Clinical Immunology and Hypertension, RWTH Aachen, Aachen, Germany
| | - Christoph Kuppe
- Department of Nephrology, Rheumatology, Clinical Immunology and Hypertension, RWTH Aachen, Aachen, Germany
| | - Ritabrata Sanyal
- Department of Nephrology, Rheumatology, Clinical Immunology and Hypertension, RWTH Aachen, Aachen, Germany
| | - Sikander Hayat
- Department of Nephrology, Rheumatology, Clinical Immunology and Hypertension, RWTH Aachen, Aachen, Germany
| | - Luis Felipe Casas Murillo
- Computer Science, University of Texas at Dallas, USA
- Robotic Systems Engineering, RWTH Aachen University, Aachen, Germany
| | - Turkan Hajili
- Department of Urology and Pediatric Urology, University Hospital, RWTH Aachen University, Aachen, Germany
| | - Marco Hoffmann
- Department of Urology and Pediatric Urology, University Hospital, RWTH Aachen University, Aachen, Germany
| | - Matthias Saar
- Department of Urology and Pediatric Urology, University Hospital, RWTH Aachen University, Aachen, Germany
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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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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Isogai M, Hamamoto S, Kawase K, Okada T, Hattori T, Chaya R, Hamakawa T, Sugino T, Taguchi K, Umemoto Y, Okada A, Yasui T. Efficacy of ultrasound monitoring during extracorporeal shock wave lithotripsy: A multi‐institutional propensity score‐matched study. Int J Urol 2022; 29:1054-1060. [DOI: 10.1111/iju.14984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 06/30/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Masahiko Isogai
- Department of Nephro‐Urology Nagoya City University Graduate School of Medical Sciences Nagoya Japan
- Department of Urology Nagoya City University West Medical Center Nagoya Japan
| | - Shuzo Hamamoto
- Department of Nephro‐Urology Nagoya City University Graduate School of Medical Sciences Nagoya Japan
| | - Kengo Kawase
- Department of Nephro‐Urology Nagoya City University Graduate School of Medical Sciences Nagoya Japan
| | - Tomoki Okada
- Department of Nephro‐Urology Nagoya City University Graduate School of Medical Sciences Nagoya Japan
| | - Tatsuya Hattori
- Department of Nephro‐Urology Nagoya City University Graduate School of Medical Sciences Nagoya Japan
| | - Ryosuke Chaya
- Department of Nephro‐Urology Nagoya City University Graduate School of Medical Sciences Nagoya Japan
| | - Takashi Hamakawa
- Department of Urology Nagoya City University West Medical Center Nagoya Japan
| | - Teruaki Sugino
- Department of Nephro‐Urology Nagoya City University Graduate School of Medical Sciences Nagoya Japan
| | - Kazumi Taguchi
- Department of Nephro‐Urology Nagoya City University Graduate School of Medical Sciences Nagoya Japan
| | - Yukihiro Umemoto
- Department of Urology Nagoya City University West Medical Center Nagoya Japan
| | - Atsushi Okada
- Department of Nephro‐Urology Nagoya City University Graduate School of Medical Sciences Nagoya Japan
| | - Takahiro Yasui
- Department of Nephro‐Urology Nagoya City University Graduate School of Medical Sciences Nagoya Japan
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