PD26-02 FLUOROSCOPIC TARGETING OF RENAL CALCULI DURING EXTRACORPOREAL SHOCKWAVE LITHOTRIPSY USING A MACHINE LEARNING ALGORITHM
Bibliographic record
Abstract
You have accessJournal of UrologyStone Disease: Shock Wave Lithotripsy (PD26)1 Apr 2019PD26-02 FLUOROSCOPIC TARGETING OF RENAL CALCULI DURING EXTRACORPOREAL SHOCKWAVE LITHOTRIPSY USING A MACHINE LEARNING ALGORITHM Rohit Singla, Colin Lundeen, Connor Forbes*, David Hogarth, and Christopher Nguan Rohit SinglaRohit Singla More articles by this author , Colin LundeenColin Lundeen More articles by this author , Connor Forbes*Connor Forbes* More articles by this author , David HogarthDavid Hogarth More articles by this author , and Christopher NguanChristopher Nguan More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555962.29512.0bAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: The efficacy of Extracorporeal Shockwave Lithotripsy (ESWL) is influenced by the time spent delivering focused energy to the stone. Manual targeting occurs at the start of the procedure, but subsequent respiration and movement significantly reduce the time that the stone is in the crosshairs. Radiographic appearance varies between stones and may change during treatment which makes targeting difficult. These effects result in increased radiation exposure and operative duration with increased shockwave exposure to surrounding structures in addition to decreased efficacy of stone fragmentation. There is a need for improved stone targeting to improve care. We propose a computer vision algorithm to locate stones during ESWL treatment. METHODS: 2413 fluoroscopic images from n=102 subjects that underwent ESWL were manually annotated followed by secondary review for annotation agreement (CL, CF, DH). A bounding box was drawn around any stone present. The algorithm RetinaNet was trained using a random split of n=90 subjects and tested on n=12. This was repeated for 10 unique splits. The mean Average Precision (AP) and stone detection time are reported. RESULTS: Over 10 trials, the mean (+/- stdev) AP was 0.7 ± 0.1, indicating that in 1 of every 1.4 images the algorithm was able to locate the stone to within 50% of the annotation. Detection failure was attributed to small target size (<5% of image) or blurry image due to machine motion. The average (+/- stdev) detection time was 63 ± 1ms. CONCLUSIONS: An algorithm to automatically detect urinary tract stones during ESWL is presented, achieving ample precision to further develop an active targeting system. This work will be integrated into a broader artificial intelligence system for stone detection, automatic targeting and real-time in-procedure ESWL tracking for optimized outcomes. Source of Funding: none Vancouver, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e474-e474 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Rohit Singla More articles by this author Colin Lundeen More articles by this author Connor Forbes* More articles by this author David Hogarth More articles by this author Christopher Nguan More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".