Automated reading of values from a breath sample analyser: development and validation of a vision-language model–based image analysis pipeline

Alex Au Yong; Hok-Fung Tong; Ming Sun; Johan Conradie

doi:10.1016/j.pathol.2026.01.169

February 1, 2026Pathology

Automated reading of values from a breath sample analyser: development and validation of a vision-language model–based image analysis pipeline

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Key Points

This research aims to develop and validate an image analysis pipeline for breath sample analysers.
Developed a vision-language model-based pipeline for image analysis.
Validated the accuracy and efficiency of the AI model in reading breath samples.
Demonstrated that the automated system reduces the time required for transcription.
Showed improved accuracy in recording results compared to manual methods.

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Implication

Automated pipeline applies AI for reading breath samples, indicating potential to reduce errors in transcription.

Abstract

Background: Recording results from laboratory instruments lacking digital connectivity requires manual transcription, which is time-consuming and prone to errors. 1 Vision-language models (VLMs), artificial intelligence (AI) models which can process images, could be used to automate this task.

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Cite This Study

Yong et al. (2026) studied this question.

synapsesocial.com/papers/699a534bfdaf4e3c1268ef2d https://doi.org/10.1016/j.pathol.2026.01.169

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Automated reading of values from a breath sample analyser: development and validation of a vision-language model–based image analysis pipeline

Key Points

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Implication

Abstract

Discussion

Cite This Study

Automated reading of values from a breath sample analyser: development and validation of a vision-language model–based image analysis pipeline

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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