Analytical validation of 39 clinical chemistry tests and 17 immunoassays on the Alinity analytical system

Šegulja, Dragana; Dukić, Kristina; Bogić, Anamarija; Vrančić, Ana Lončar; Komljenović, Sven; Šparakl, Tajana; Teodorović, Katarina Grdiša; Kurajica, Vlasta Cigula; Lapić, Ivana; Oguić, Saša Kralik; Kozmar, Ana; Vogrinc, Željka; Rogić, Dunja

doi:10.1080/00365513.2022.2056856

Cited by 2 publications

(1 citation statement)

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“…Firstly, differences arise at the level of analytical measurements, where, even for the same biomarker, various laboratories and medical facilities may employ reagents or platforms from different suppliers. Studies indicate that variations would exist between assays for the same biomarker on different analytical kits, emphasizing the need for extensive and rigorous validation before clinical implementation [ 91 ]. Additionally, discrepancies in the biomarkers that are tested in the panel also pose computational challenges and interpretation issues for AI models.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

Wang,

Lin,

Zhou

et al. 2024

Cancers

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The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

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Section: Challenges and Opportunitiesmentioning

confidence: 99%