Development, validation and application of a 3D printed model depicting adenoid hypertrophy in comparison to a Nasoendoscopy
Bibliographic record
Abstract
BACKGROUND: The exploration of tridimensional (3D) technology of computational tomography and the development of valid 3D printed models may improve the assessment of adenoid obstruction. The identification of an enlarged adenoid in childhood would streamline the referral of appropriately selected cases to an otolaryngologist, leading to early treatment of affected children when indicated. The objective of this study is to validate the use of a 3D printed model depicting adenoid hypertrophy based on the pediatric otolaryngologist, head and neck surgeon (OHNS) participants assessment. METHODS: A cross-sectional study was performed to develop and validate 3D depictions, including print-outs, of the nasopharynx including different degrees of Adenoidal Hypertrophy (AH). The print-outs were obtained from 14 Cone-beam computed tomography (CBCT) scans of 14 children (12 boys, 2 girls; mean age of 10.61 years) representing grades 1, 2, 3, and 4 nasopharyngeal adenoidal obstructions, according to a previously Nasoendoscopy-graded (NE) classification by a licensed OHNS. The prevalence of AH in this study was 36%. Two OHNS were recruited to assess the print-outs representing two different representations of the nasopharyngeal airway, the lumen (LU) and adenoid mass (AD). LU and AD were visualized in 2D - pictures- and in 3D - printed prototypes. Intraclass correlation was used to assess intra- and inter-reliability. The validity of our depictions was analyzed through comparison (accuracy and correlation) to the reference standard (NE). The data were clustered to calculate the sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV). Cross-tab and Pearson's T-test were performed. RESULTS: Overall, the 3D depiction tools showed different diagnostic capabilities. AD representations showed strong (AD 2D, 75%) to almost perfect (AD 3D, 88%) accuracy compared to NE. Excellent sensitivity and specificity were observed for the AD 3D (100, 70%), as well as adequate PPV and NPV (66 and 97% respectively), with only 5% of false-negative cases. CONCLUSION: The use of Dolphin software for the acquisition of a 3D printed prototype of the nasopharyngeal adenoidal region seems promising. These prototypes may be a practical and readily available alternative for the assessment of the nasopharyngeal obstructed area. CBCT in children must be taken under strong solid indications. Early referral to an OHNS for a full assessment remains the main objective in children with unclear symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".