PD58-04 MODELING AUTOMATED ASSESSMENT OF SURGICAL PERFORMANCE UTILIZING COMPUTER VISION: PROOF OF CONCEPT
Notice bibliographique
Résumé
You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment II1 Apr 2018PD58-04 MODELING AUTOMATED ASSESSMENT OF SURGICAL PERFORMANCE UTILIZING COMPUTER VISION: PROOF OF CONCEPT Amir Baghdadi, Lora Cavuoto, Ahmed Aly Hussein, Youssef Ahmed, and Khurshid Guru Amir BaghdadiAmir Baghdadi More articles by this author , Lora CavuotoLora Cavuoto More articles by this author , Ahmed Aly HusseinAhmed Aly Hussein More articles by this author , Youssef AhmedYoussef Ahmed More articles by this author , and Khurshid GuruKhurshid Guru More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.2792AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Thorough lymph node dissection (LND) is an integral part of robot-assisted radical cystectomy (RARC). There is a lack of consensus about what constitutes adequate LND. Current methods are subject to inter-rater variability. In this context, we sought to use computer vision methods to identify and extract valid measures to develop and validate an automated scoring system for LND. METHODS 20 recorded LNDs were included with a total of 200 frames/case from the console feed before and after LND with near-equivalent view and zoom. The quality of lymph node clearance was assessed based on the features derived from a computer vision algorithm: the number and area of the nerve/vessels (N-Vs) detected using the proposed Automated Structure Detection (ASD) method; image median Color Map by the assumption of decrease in yellow color after lymphatic and fatty tissue removal; and mean entropy, which measures the level of disorganization in the image. Each video frame was pre-processed for N-Vs detection using a series of image processing operations including binary conversion, edge and line detection, and object identification while considering geometrical characteristics of target objects, e.g. aspect ratio, width, height, and orientation. The N-Vs were labeled by fusing the information from both line and object detection processes (Figure 1). The automated scores (AS) were compared to Pelvic Lymphadenectomy Appropriateness and Completion Evaluation (PLACE), which is a subjective evaluation based on objective measures scored by a panel of expert surgeons. Logistic regression analysis was employed to compare AS and PLACE scores. RESULTS 14 cases were used to develop the automated scoring algorithm. A logistic regression model was trained and validated using the aforementioned features with 30% holdout cross validation. This model was applied to the remaining 6 previously unseen cases for testing and the accuracy of predicting the PLACE scores was 83.3% (5 correct score allocation across the 6 test cases). CONCLUSIONS To our knowledge,this is the first automated surgical skill assessment tool that provides objective evaluation of surgical performance with high accuracy compared to expert surgeon assessment. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e1134-e1135 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Amir Baghdadi More articles by this author Lora Cavuoto More articles by this author Ahmed Aly Hussein More articles by this author Youssef Ahmed More articles by this author Khurshid Guru More articles by this author Expand All Advertisement 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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,025 | 0,006 |
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 ».