Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-Resource Setting: An Effectiveness Evaluation in South Sudan
Notice bibliographique
Résumé
To evaluate accuracy of child stature (height/length) and mid-upper arm circumference (MUAC) measurements produced by the AutoAnthro 3D imaging system developed by Body Surface Technology Inc following improvements to the software algorithm to improve accuracy and support automated processing, and hardware changes aimed to reduce cost. A two-stage cluster survey in Malakal Protection of Civilians (PoC) in South Sudan between September 27 and October 2, 2021. All children aged 6–59 months within selected households were eligible. For each child, manual measurements were obtained by two anthropometrists following the protocol used for the 2006 WHO Child Growth Standards (CGS) study. Scans were then captured by a different enumerator using a Samsung Galaxy 8 phone loaded with a custom software, AutoAnthro, and an Intel RealSense 3D scanner. Scans were processed using a fully automated algorithm. A multivariate logistic regression was fit to evaluate adjusted odds of achieving a successful scan. Accuracy of measurements were visually assessed using Bland-Altman (BA) plots and quantified using average bias, technical error of measurement (TEM), limits of agreement (LoA), and the 95% precision interval for individual differences. Manual measurements were obtained for 539 age eligible children, from which scan derived measurements were successfully processed for 234 (43.4%) of children. Caregivers for at least 56 children (10.4%) refused consent for scan capture; additional scans were unsuccessfully transmitted to the server. Neither demographic characteristics of the children (age and sex), stature, nor MUAC were associated with availability of scan derived measurements (P > 0.05); team was significantly associated (P < 0.001). The average bias of measurements in cm was −0.5 (95% confidence interval (CI): −2.0, 1.0) for stature and + 0.7 (CI: 0.4, 1.0) for MUAC. For stature, 95% LoA was −23.9 to 22.9 cm. For MUAC, the 95% LoA was −4.0 to 5.4 cm. The TEM was 8.4 cm for stature and 1.8 cm for MUAC. All metrics of accuracy varied considerably by team. Scan derived measurements were not of sufficient accuracy for widespread adoption. Differences in accuracy by team provide evidence that investments in training may be able to improve performance. USAID's Bureau for Humanitarian Affairs and Grand Challenges Canada.
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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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 ».