Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics
<p>Machine learning (ML)-enabled point-of-care testing (POCT) readers are transforming rapid diagnostics by reducing subjectivity in visual interpretation, enabling quantification where appropriate, and extending results into connected clinical pathways. This framework-oriented narrative review and...
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| Autor principal: | |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2026
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/1249430 |
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| _version_ | 1879164897931034624 |
|---|---|
| author | Pereira, Paulo |
| author_browse | Pereira, Paulo |
| author_facet | Pereira, Paulo |
| author_sort | Pereira, Paulo |
| building | IntechOpen |
| collection | Acceso abierto |
| description | <p>Machine learning (ML)-enabled point-of-care testing (POCT) readers are transforming rapid diagnostics by reducing subjectivity in visual interpretation, enabling quantification where appropriate, and extending results into connected clinical pathways. This framework-oriented narrative review and perspective describe the technical and clinical foundations of ML-enabled readers, with emphasis on lateral-flow assays as the archetypal rapid format. We outline reader architectures, computer-vision pipelines, and calibration strategies that translate strip signals to clinically actionable outputs with uncertainty. Because ML becomes part of the measurement system, we propose an evaluation framework that integrates analytical performance (e.g., precision, robustness, interference, low-signal sensitivity) with clinical validation, human factors, interoperability, and continuous monitoring in the post-deployment environment. We align recommendations with laboratory and POCT quality standards, proficiency testing and accreditation guidance, and emerging regulatory expectations for AI-driven medical device software. Finally, we provide implementation case studies and a practical checklist to support the safe scale-up of ML-enabled POCT readers across decentralized settings.</p> |
| doi_str_mv | 10.5772/intechopen.1015282 |
| first_indexed | 2026-10-15T21:41:38Z |
| format | Book Chapter |
| fullrecord | <oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><identifier>intech-1249430</identifier><datestamp>2026-06-02</datestamp>
<dc:title>Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics</dc:title>
<dc:creator>Paulo Pereira</dc:creator>
<dc:subject>Health Sciences</dc:subject>
<dc:description><p>Machine learning (ML)-enabled point-of-care testing (POCT) readers are transforming rapid diagnostics by reducing subjectivity in visual interpretation, enabling quantification where appropriate, and extending results into connected clinical pathways. This framework-oriented narrative review and perspective describe the technical and clinical foundations of ML-enabled readers, with emphasis on lateral-flow assays as the archetypal rapid format. We outline reader architectures, computer-vision pipelines, and calibration strategies that translate strip signals to clinically actionable outputs with uncertainty. Because ML becomes part of the measurement system, we propose an evaluation framework that integrates analytical performance (e.g., precision, robustness, interference, low-signal sensitivity) with clinical validation, human factors, interoperability, and continuous monitoring in the post-deployment environment. We align recommendations with laboratory and POCT quality standards, proficiency testing and accreditation guidance, and emerging regulatory expectations for AI-driven medical device software. Finally, we provide implementation case studies and a practical checklist to support the safe scale-up of ML-enabled POCT readers across decentralized settings.</p></dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2026-06-02</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/1249430</dc:identifier>
<dc:identifier>doi:10.5772/intechopen.1015282</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-1-80631-376-1</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/1005364 ; Point-of-Care Testing - Emerging Technologies, Analytical Performance, and Clinical Integration</dc:source>
</oai_dc:dc> |
| id | intech-1249430 |
| institution | IntechOpen |
| isbn | 978-1-80631-376-1 |
| language | English |
| last_indexed | 2026-10-15T21:41:38Z |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | oai_dc |
| spelling | intech-12494302026-06-02 Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics Paulo Pereira Health Sciences <p>Machine learning (ML)-enabled point-of-care testing (POCT) readers are transforming rapid diagnostics by reducing subjectivity in visual interpretation, enabling quantification where appropriate, and extending results into connected clinical pathways. This framework-oriented narrative review and perspective describe the technical and clinical foundations of ML-enabled readers, with emphasis on lateral-flow assays as the archetypal rapid format. We outline reader architectures, computer-vision pipelines, and calibration strategies that translate strip signals to clinically actionable outputs with uncertainty. Because ML becomes part of the measurement system, we propose an evaluation framework that integrates analytical performance (e.g., precision, robustness, interference, low-signal sensitivity) with clinical validation, human factors, interoperability, and continuous monitoring in the post-deployment environment. We align recommendations with laboratory and POCT quality standards, proficiency testing and accreditation guidance, and emerging regulatory expectations for AI-driven medical device software. Finally, we provide implementation case studies and a practical checklist to support the safe scale-up of ML-enabled POCT readers across decentralized settings.</p> IntechOpen 2026-06-02 Chapter, Part Of Book https://www.intechopen.com/chapters/1249430 doi:10.5772/intechopen.1015282 en ISBN:978-1-80631-376-1 https://creativecommons.org/licenses/by/3.0/ https://www.intechopen.com/books/1005364 ; Point-of-Care Testing - Emerging Technologies, Analytical Performance, and Clinical Integration |
| spellingShingle | Health Sciences Pereira, Paulo Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics |
| title | Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics |
| title_full | Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics |
| title_fullStr | Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics |
| title_full_unstemmed | Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics |
| title_short | Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics |
| title_sort | machine learning enabled poct readers from lateral flow interpretation to quantitative connected diagnostics |
| topic | Health Sciences |
| url | https://www.intechopen.com/chapters/1249430 |
| work_keys_str_mv | AT pereirapaulo machinelearningenabledpoctreadersfromlateralflowinterpretationtoquantitativeconnecteddiagnostics |