Integration of artificial intelligence and plasma steroidomics with laboratory information management systems: application to primary aldosteronism
| العنوان: | Integration of artificial intelligence and plasma steroidomics with laboratory information management systems: application to primary aldosteronism |
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| المؤلفون: | Georgiana Constantinescu, Manuel Schulze, Mirko Peitzsch, Thomas Hofmockel, Ute I. Scholl, Tracy Ann Williams, Jacques W.M. Lenders, Graeme Eisenhofer |
| المصدر: | Clinical Chemistry and Laboratory Medicine, 60, 12, pp. 1929-1937 Clinical Chemistry and Laboratory Medicine, 60, 1929-1937 |
| بيانات النشر: | Walter de Gruyter GmbH, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Male, G Protein-Coupled Inwardly-Rectifying Potassium Channels, Artificial Intelligence, Information Management, Hyperaldosteronism, Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], Biochemistry (medical), Clinical Biochemistry, Humans, Female, Steroids, General Medicine, Mass Spectrometry |
| الوصف: | Objectives Mass spectrometry-based steroidomics combined with machine learning (ML) provides a potentially powerful approach in endocrine diagnostics, but is hampered by limitations in the conveyance of results and interpretations to clinicians. We address this shortcoming by integration of the two technologies with a laboratory information management systems (LIMS) model. Methods The approach involves integration of ML algorithm-derived models with commercially available mathematical programming software and a web-based LIMS prototype. To illustrate clinical utility, the process was applied to plasma steroidomics data from 22 patients tested for primary aldosteronism (PA). Results Once mass spectrometry data are uploaded into the system, automated processes enable generation of interpretations of steroid profiles from ML models. Generated reports include plasma concentrations of steroids in relation to age- and sex-specific reference intervals along with results of ML models and narrative interpretations that cover probabilities of PA. If PA is predicted, reports include probabilities of unilateral disease and mutations of KCNJ5 known to be associated with successful outcomes of adrenalectomy. Preliminary results, with no overlap in probabilities of disease among four patients with and 18 without PA and correct classification of all four patients with unilateral PA including three of four with KCNJ5 mutations, illustrate potential utility of the approach to guide diagnosis and subtyping of patients with PA. Conclusions The outlined process for integrating plasma steroidomics data and ML with LIMS may facilitate improved diagnostic-decision-making when based on higher-dimensional data otherwise difficult to interpret. The approach is relevant to other diagnostic applications involving ML. |
| وصف الملف: | application/pdf |
| تدمد: | 1437-4331 1434-6621 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f3c1deb3aa25b674bc932d707b6242e https://doi.org/10.1515/cclm-2022-0470 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....4f3c1deb3aa25b674bc932d707b6242e |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14374331 14346621 |
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