PD26-02 FLUOROSCOPIC TARGETING OF RENAL CALCULI DURING EXTRACORPOREAL SHOCKWAVE LITHOTRIPSY USING A MACHINE LEARNING ALGORITHM
Notice bibliographique
Résumé
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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,056 | 0,015 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».