Three-dimensional markerless surface topography approach with convolutional neural networks for adolescent idiopathic scoliosis screening
Notice bibliographique
Résumé
Adolescent idiopathic scoliosis (AIS) is a three-dimensional lateral and torsional deformity of the spine, affecting up to 5% of the population. Traditional scoliosis screening methods exhibit limited accuracy, leading to unnecessary referrals and exposure to ionizing radiation from x-ray examinations. The 3D markerless surface topography (ST) technique quantifies trunk asymmetry and can be a potential scoliosis screening tool. However, differences in trunk asymmetry between individuals with scoliosis and those with a typically developing spine have yet to be thoroughly studied. Using the ST method, this study aims to distinguish adolescents with AIS from those with typically a developing spine. Participants aged 10 to 18 years, comprising of 285 individuals with confirmed AIS and 273 with typically developing spines, were included in the study (total scans including follow-ups: 693 for the AIS group and 298 for the control group). The positive for AIS group was identified through radiographic exams, specifically with curves ranging from 10° to 45°, while the negative (control) group qualified if their scoliometer test measured less than 7° and they had no known scoliosis diagnosis. The dataset comprised of surface torso scans captured either using stationary Minolta cameras or with the Structure sensor. ST analysis involved the reflection of the 3D geometry of the torso, aligning it with the original torso by minimizing the distance between corresponding points. Deviations between the original and reflected torso over the back surface and torso surface depth were mapped onto 102 × 102 grids. A convolutional neural network (CNN) was developed using deviations and depth (distance between the back surface and frontal plane) maps as inputs to classify the torso surface of typically developing adolescents and those with AIS. 10-fold cross-validation was applied during model development. 20% of the data was used as a holdout for final testing. Classification results of the proposed model were compared to the ground truth. The average training and validation accuracy across the ten folds was 100% and 94%, respectively. The classifications from the testing sets using the best performing model from the 10-fold cross-validation obtained accuracy, sensitivity, and specificity of 95%, 97%, and 90%, respectively. The positive likelihood ratio (PLR) of the testing set was 9.7. Likewise, a negative likelihood ratio (NLR) of 0.032 was also attained. The model sensitivity for detecting curves with Cobb greater than 25° was 99%. The sensitivity for detecting mild cases (Cobb < 25°) was 96%. The proposed CNN predictive model to detect AIS using ST showed excellent classification results. Markerless surface topography can serve as a dependable and non-invasive method for screening AIS.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 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 | 0,001 | 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) | 0,002 | 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 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 ».