Development and performance validation of a low-cost algorithms-based hyperspectral imaging system for radiodermatitis assessment
Bibliographic record
Abstract
Whilst radiotherapy (RT) is widely used for cancer treatment, radiodermatitis caused by RT is one most common severe side effect affecting 95% cancer patients. Accurate radiodermatitis assessment and classification is essential to adopt timely treatment, management and monitoring, which all depend on reliable and objective tools for radiodermatitis grading. We therefore, in this work, reported the development and grading performance validation of a low-cost (∼2318.2 CNY) algorithms-based hyperspectral imaging (aHSI) system for radiodermatitis assessment. The low-cost aHSI system was enabled through Monte Carlo (MC) simulations conducted on multi-spectra acquired from a custom built low-cost multispectral imaging (MSI) system, deriving algorithms-based hyper-spectra with spectral resolution of 1 nm. The MSI system was based on sequentially illuminated narrow-band light-emitting diodes (LEDs) and a CMOS camera. Erythema induced artificially on healthy volunteers was measured by the aHSI system developed, with algorithms-based hyper-spectra and skin layer resolved physiological parameters (i.e., the blood volume fraction (BVF) and the oxygen saturation of hemoglobin in blood, et. al.) derivation using MC simulations. The MC simulations derived BVF and the oxygen saturation of hemoglobin in blood showed significant (P < 0.001, analysis of variance: ANOVA) increase with erythema. Further 1D-convolution neural network (CNN) implemented on the algorithms-based hyper-spectra leads to an overall classification accuracy of 93.1%, suggesting the great potential of low-cost aHSI system developed for radiodermatitis assessment.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".