VIRTUAL REALITY FOR PATIENT-SPECIFIC MULTIDISCIPLINARY PLANNING OF COMPLEX ORTHOPAEDIC ONCOLOGICAL SURGERY
Notice bibliographique
Résumé
Surgical planning for complex orthopaedic oncology cases requires thorough understanding of anatomy and relationships to critical structures. Current imaging regimens include 2D planar CT and MRI, multiplanar reformations, 3D surface enhanced series and 3D printed models. However, Virtual Reality (VR) visualization and planning is a dynamic, immersive experience offering enhanced understanding of complex anatomical relationships, the ability manipulate and layer 3D models and images in real time and allows for multiple participants to engage simultaneously across remote sites. This pilot describes our initial experience with a novel VR planning system. Six patients with complex anatomic tumors were reviewed preoperatively by a multidisciplinary team of orthopaedic oncology surgeons, neurosurgeons, thoracic surgeons, spine surgeons along with a musculoskeletal radiologist. Interactive, on demand modelling of anatomical structures was performed in real time with input from all team members. Three cases involved the chest wall and spine and three cases arose from the pelvis. Pathology included Ewings sarcoma, neuroblastoma, and chondrosarcoma. Three-dimensional models of relevant bony and soft tissue anatomy were derived directly from diagnostic CT and MR imaging using 3D modeling tools in the virtual environment while noting the length of time and cost to prepare cases. The VR system was also validated for model and measurement accuracy via direct comparisons of with a reference system, and for overall ease of use and clinical utility. The Dice-Srensen Coefficient (DSC) was used to score similarity of models generated in VR to those made in the reference platform. On multidisciplinary debrief, each case reviewed in VR provided added information to the surgical team compared to standard imaging. This included better understanding of the tumor margins and relationships to critical structures and, in three cases, modified surgical approach. The platform successfully supported multiple reviewers sharing the same VR environment and allowed for dynamic changes to 2D and 3D visualizations. The mean time to prepare cases was 70 minutes (range 25 −90) dependent on number of anatomical structures to be modeled, representing a mean cost per case of $233 USD (range $85 −$300). DSC values for 3D structures created in VR were 0.97 or higher, confirming geometric accuracy of models relative to a reference system. Measurements of length, cross-sectional area, and angle on clinical CT scans were within 0.22 mm (0.3%), 0.16 mm2 (0.02%), and 0.04 deg (0.07%) or less of expected results, respectively. Lastly, pre-defined usability tests were successfully conducted by 15 volunteer end-users, all of whom yielded accurate measurements and models, and reported high confidence in their use of the platform. VR planning of complex multidisciplinary cases is dynamic, feasible and cost effective providing enhanced appreciation of complex anatomical relationships, leading to increased surgeon confidence and impacting on surgical approach.
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 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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».