Deep Learning-Based Haptic Guidance for Surgical Skills Transfer
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Notice bibliographique
Résumé
Having a trusted and useful system that helps to diminish the risk of medical errors and facilitate the improvement of quality in the medical education is indispensable. Thousands of surgical errors are occurred annually with high adverse event rate, despite inordinate number of devised patients safety initiatives. Inadvertently or otherwise, surgeons play a critical role in the aforementioned errors. Training surgeons is one of the most crucial and delicate parts of medical education and needs more attention due to its practical intrinsic. In contrast to engineering, dealing with mortal alive creatures provides a minuscule chance of trial and error for trainees. Training in operative rooms, on the other hand, is extremely expensive in terms of not only equipment but also hiring professional trainers. In addition, the COVID-19 pandemic has caused to establish initiatives such as social distancing in order to mitigate the rate of outbreak. This leads surgeons to postpone some non-urgent surgeries or operate with restrictions in terms of safety. Subsequently, educational systems are affected by the limitations due to the pandemic. Skill transfer systems in cooperation with a virtual training environment is thought as a solution to address aforesaid issues. This enables not only novice surgeons to enrich their proficiency but also helps expert surgeons to be supervised during the operation. This paper focuses on devising a solution based on deep leaning algorithms to model the behavior of experts during the operation. In other words, the proposed solution is a skill transfer method that learns professional demonstrations using different effective factors from the body of experts. The trained model then provides a real-time haptic guidance signal for either instructing trainees or supervising expert surgeons. A simulation is utilized to emulate an operating room for femur drilling surgery, which is a common invasive treatment for osteoporosis. This helps us with both collecting the essential data and assessing the obtained models. Experimental results show that the proposed method is capable of emitting guidance force haptic signal with an acceptable error rate.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découle