Reproducibility of In Vivo Corneal Confocal Microscopy Using an Automated Analysis Program for Detection of Diabetic Sensorimotor Polyneuropathy
Bibliographic record
Abstract
OBJECTIVE: In vivo Corneal Confocal Microscopy (IVCCM) is a validated, non-invasive test for diabetic sensorimotor polyneuropathy (DSP) detection, but its utility is limited by the image analysis time and expertise required. We aimed to determine the inter- and intra-observer reproducibility of a novel automated analysis program compared to manual analysis. METHODS: In a cross-sectional diagnostic study, 20 non-diabetes controls (mean age 41.4±17.3y, HbA1c 5.5±0.4%) and 26 participants with type 1 diabetes (42.8±16.9y, 8.0±1.9%) underwent two separate IVCCM examinations by one observer and a third by an independent observer. Along with nerve density and branch density, corneal nerve fibre length (CNFL) was obtained by manual analysis (CNFLMANUAL), a protocol in which images were manually selected for automated analysis (CNFLSEMI-AUTOMATED), and one in which selection and analysis were performed electronically (CNFLFULLY-AUTOMATED). Reproducibility of each protocol was determined using intraclass correlation coefficients (ICC) and, as a secondary objective, the method of Bland and Altman was used to explore agreement between protocols. RESULTS: Mean CNFLManual was 16.7±4.0, 13.9±4.2 mm/mm2 for non-diabetes controls and diabetes participants, while CNFLSemi-Automated was 10.2±3.3, 8.6±3.0 mm/mm2 and CNFLFully-Automated was 12.5±2.8, 10.9 ± 2.9 mm/mm2. Inter-observer ICC and 95% confidence intervals (95%CI) were 0.73(0.56, 0.84), 0.75(0.59, 0.85), and 0.78(0.63, 0.87), respectively (p = NS for all comparisons). Intra-observer ICC and 95%CI were 0.72(0.55, 0.83), 0.74(0.57, 0.85), and 0.84(0.73, 0.91), respectively (p<0.05 for CNFLFully-Automated compared to others). The other IVCCM parameters had substantially lower ICC compared to those for CNFL. CNFLSemi-Automated and CNFLFully-Automated underestimated CNFLManual by mean and 95%CI of 35.1(-4.5, 67.5)% and 21.0(-21.6, 46.1)%, respectively. CONCLUSIONS: Despite an apparent measurement (underestimation) bias in comparison to the manual strategy of image analysis, fully-automated analysis preserves CNFL reproducibility. Future work must determine the diagnostic thresholds specific to the fully-automated measure of CNFL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".