ArticleNature communications2025
Artificial Intelligence (AI)-assisted readout method for the evaluation of skin prick automated test results.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Temporal Patterns of Allergen Sensitisation in the General Population: The LEAD Study.Clinical and translational allergy · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The skin prick test (SPT) is the gold standard for diagnosing allergic sensitization to aeroallergies. The Skin Prick Automated Test (SPAT) device has previously demonstrated reduced variability and more consistent test results compared to manual SPT. The current study aims to develop and validate an artificial intelligence (AI) assisted readout method to support physicians in interpreting skin reactions following SPAT. To train the AI algorithm, 7812 wheals (651 patients) are manually labeled. To validate the AI measurement, the longest wheal diameter of 2604 wheals (217 patients) is measured by the treating physician and compared to the AI measurement. In addition, AI-assisted readout is validated on a separate test cohort of 95 patients (1140 wheals). We demonstrate that the AI measurements of the longest wheal diameter exhibit a strong correlation with the physician's measurements. The AI algorithm shows a specificity of 98·4% and sensitivity of 85·0% in determining positive or negative test results in the validation cohort. In the test cohort, physicians adjust 5·8% of AI measurements, leading to a change in the test interpretation for only 0·5% of cases. AI-assisted readout significantly reduces inter- and intra-observer variability and readout time compared to manual physician measurements. Altogether, the AI-assisted readout method demonstrates high accuracy, with minimal misclassification of test results. Adding AI to SPAT further improves standardization across the SPT process, significantly reducing observer variability and time to readout.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.