Pd06-12 inter- and intra-rater variability of videourodynamic study interpretation among urologic surgeons for patients with spina bifida

You have accessJournal of UrologyPediatrics I (PD04)1 May 2024PD04-03 DEEP LEARNING OF VIDEOURODYNAMICS TO PREDICT INCIDENT HYDRONEPHROSIS IN PATIENTS WITH SPINA BIFIDA John Weaver, Joseph Logan, Jason Van Batavia, Dana Weiss, Christopher Long, Ariana Smith, Stephen Zderic, Zoe Gan, Karl Godlewski, Reiley Broms, Maria Antony, Madalyne Martin-Olenski, Jing Huang, Yong Fan, and Gregory Tasian John WeaverJohn Weaver , Joseph LoganJoseph Logan , Jason Van BataviaJason Van Batavia , Dana WeissDana Weiss , Christopher LongChristopher Long , Ariana SmithAriana Smith , Stephen ZdericStephen Zderic , Zoe GanZoe Gan , Karl GodlewskiKarl Godlewski , Reiley BromsReiley Broms , Maria AntonyMaria Antony , Madalyne Martin-OlenskiMadalyne Martin-Olenski , Jing HuangJing Huang , Yong FanYong Fan , and Gregory TasianGregory Tasian View All Author Informationhttps://doi.org/10.1097/01.JU.0001008812.05762.7f.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: High inter-observer variability in interpreting videourodynamic (VUDS) studies limits their effectiveness in reliably classifying risk of kidney injury. We developed machine learning models that predict incident hydronephrosis (HDN) in patients with spina bifida (SB) based on VUDS data. METHODS: We performed a retrospective cohort study of patients aged 2 months to 41 years with SB who underwent VUDS at a single institution between 2016 and 2022. The outcome was incident HDN, a surrogate outcome for chronic kidney disease (CKD) progression. Patients entered the cohort at the first VUDS study after birth. We built 4 models to predict incident HDN: (1) a random forest clinical model using clinical data abstracted from VUDS studies by urologists, (2) a deep learning convolutional neural network of data from bladder volume-pressure recordings, (3) a deep learning imaging model of fluoroscopic images of the bladder, (4) an ensemble model averaging the risk probabilities of the volume-pressure and fluoroscopic models. RESULTS: We included 354 patients followed for a median of 3.1 (IQR 2, 4.1) years. (Table 1) The ensemble model that included only studies that reached>75% expected bladder capacity had the best prognostication (C-index 0.73; 95% CI 0.68-0.76). The performance of the model using data abstracted by urologists was the poorest (C-index 0.64; 95% CI 0.62-0.69). (Table 2) When risk scores generated by the 75% ensemble model were used to stratify the cohort into high-risk (top 10%) and low-risk (bottom 90%), the specificity of incident HDN in the high-risk group was 97%. (Table 3). CONCLUSIONS: Automated extraction of features from pressure/volume recordings and fluoroscopic images of the bladder accurately predicted incident HDN in patients with SB. Source of Funding: Pennsylvania Department of Health © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e83 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information John Weaver More articles by this author Joseph Logan More articles by this author Jason Van Batavia More articles by this author Dana Weiss More articles by this author Christopher Long More articles by this author Ariana Smith More articles by this author Stephen Zderic More articles by this author Zoe Gan More articles by this author Karl Godlewski More articles by this author Reiley Broms More articles by this author Maria Antony More articles by this author Madalyne Martin-Olenski More articles by this author Jing Huang More articles by this author Yong Fan More articles by this author Gregory Tasian More articles by this author Expand All Advertisement PDF downloadLoading ... 

Pd04-03 deep learning of videourodynamics to predict incident hydronephrosis in patients with spina bifida

Genetic analysis of two bladder exstrophy populations of South Asian and North American origin.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Reiley Broms

Author Tools

Chat about Author

Papers

Pd06-12 inter- and intra-rater variability of videourodynamic study interpretation among urologic surgeons for patients with spina bifida

Pd04-03 deep learning of videourodynamics to predict incident hydronephrosis in patients with spina bifida

Genetic analysis of two bladder exstrophy populations of South Asian and North American origin.