Image processing of infrared thermal images for the detection of necrotizing enterocolitis
Bibliographic record
Abstract
Necrotizing Enterocolitis (NEC) is a devastating intestinal disease associated with a high rate of mortality and long-term morbidity. Treatments can be successful if NEC is diagnosed early, but no reliable methods exist. Infrared imaging can detect tissue inflammation and thus has the potential to be an early diagnostic tool for NEC. Infants with no clinical or radiographic signs of NEC, and a group of infants with evidence of at least Bell’s Stage 2 NEC were enrolled in our study. Infants underwent bedside infrared imaging for 60 seconds. The dataset consists of twenty normal infants and nine infants with NEC. In early work, in infants with NEC, the upper-to-lower (UL) region temperatures differed significantly, where no significant difference in the UL region was found in normal infants. No significant difference was found in left-to-right (LR) region temperatures for both groups. The decision tree classifier produced good results in terms of specificity, sensitivity, and standard deviation for ten trials. Results for the medians were: 91%+/-0.07%; 84%+/-18%; and for the means they were: 86%+/-0.04%; 79%+/-21% [1]. In this work, we assessed the impact of image enhancement in discriminating between infants with NEC and those without. The approaches explored were: (i) noise reduction; (ii) background removal; and (iii) contrast enhancement. Preliminary results show marked improvement in detecting infants with NEC. Future work will automate the analysis and carry-out a prospective study to attempt detecting NEC at earlier stages. Other image analysis techniques will be tested to enhance the performance of our new diagnostic tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".