Beyond the human eye: Artificial intelligence revolutionizing plasma quality control.

IF 1.6 4区医学 Q3 HEMATOLOGY

Vox Sanguinis Pub Date : 2025-09-29 DOI:10.1111/vox.70122

Kriangsak Jenwitheesuk, Poonsup Sripara, Komsan Sayan, Wanut Padee, Anupong Tita, Ronnarit Boonyarat

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引用次数: 0

Abstract

Background and objectives: Plasma quality control (QC) in transfusion medicine relies on subjective visual inspections, which vary depending on the technologist's experience and lighting conditions, potentially compromising safety. We developed an automated system to standardize the detection of plasma colour and turbidity abnormalities, and aimed to evaluate its accuracy and reliability in a transfusion setting.

Materials and methods: At the Blood Transfusion Center, Khon Kaen University, Thailand, three experienced technologists classified 973 plasma bags. The system was trained on 789 bags (467 normal, 322 abnormal) and tested on 184 bags (145 normal, 39 abnormal) in Phase 1 and 486 bags (287 normal, 199 abnormal) in Phase 2. A sorting machine captured images under controlled lighting, and a deep learning model assessed the quality. Performance was measured using accuracy, sensitivity, specificity and precision, with 95% confidence intervals (CIs).

Results: The system achieved 87.5% accuracy (95% CI: 82.7%-91.4%) in Phase 1 and 94.7% accuracy (95% CI: 92.3%-96.3%) in Phase 2, with 100% sensitivity (95% CI: 98.1%-100%) and 90.9% specificity (95% CI: 87.1%-93.7%) in Phase 2, ensuring no abnormal units were missed. Cross-validation on the training set yielded 94.8% accuracy.

Conclusion: This automated QC system offers a reliable alternative to manual inspection, minimizing errors and reducing wastage (26 units vs. 29-86 with manual methods). Its high sensitivity and integration with a sorting machine support its potential to standardize plasma QC, enhancing patient safety and blood bank efficiency.

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超越人眼：人工智能彻底改变等离子质量控制。

背景和目的：输血医学中的血浆质量控制（QC）依赖于主观的视觉检查，这取决于技术人员的经验和照明条件，可能会损害安全性。我们开发了一个自动化系统来标准化检测血浆颜色和浊度异常，旨在评估其在输血环境中的准确性和可靠性。材料和方法：在泰国孔敬大学输血中心，三位经验丰富的技术人员对973个血浆袋进行了分类。该系统在第一阶段对789个袋子（467个正常，322个异常）进行了训练，在第二阶段对184个袋子（145个正常，39个异常）和486个袋子（287个正常，199个异常）进行了测试。一台分拣机在受控光线下捕捉图像，一个深度学习模型评估图像质量。使用准确度、灵敏度、特异性和精密度测量性能，95%置信区间（ci）。结果：该系统1期准确率为87.5% (95% CI: 82.7% ~ 91.4%)， 2期准确率为94.7% (95% CI: 92.3% ~ 96.3%)， 2期灵敏度为100% (95% CI: 98.1% ~ 100%)，特异性为90.9% (95% CI: 87.1% ~ 93.7%)，确保未遗漏异常单位。在训练集上交叉验证的准确率为94.8%。结论：该自动化QC系统为人工检测提供了可靠的替代方案，最大限度地减少了错误和浪费（26个单位，而人工方法为29-86个）。它的高灵敏度和与分选机的集成支持其标准化血浆质量控制的潜力，提高患者安全和血库效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Vox Sanguinis 医学-血液学

CiteScore

4.40

自引率

11.10%

发文量

156

审稿时长

6-12 weeks

期刊介绍： Vox Sanguinis reports on important, novel developments in transfusion medicine. Original papers, reviews and international fora are published on all aspects of blood transfusion and tissue transplantation, comprising five main sections: 1) Transfusion - Transmitted Disease and its Prevention: Identification and epidemiology of infectious agents transmissible by blood; Bacterial contamination of blood components; Donor recruitment and selection methods; Pathogen inactivation. 2) Blood Component Collection and Production: Blood collection methods and devices (including apheresis); Plasma fractionation techniques and plasma derivatives; Preparation of labile blood components; Inventory management; Hematopoietic progenitor cell collection and storage; Collection and storage of tissues; Quality management and good manufacturing practice; Automation and information technology. 3) Transfusion Medicine and New Therapies: Transfusion thresholds and audits; Haemovigilance; Clinical trials regarding appropriate haemotherapy; Non-infectious adverse affects of transfusion; Therapeutic apheresis; Support of transplant patients; Gene therapy and immunotherapy. 4) Immunohaematology and Immunogenetics: Autoimmunity in haematology; Alloimmunity of blood; Pre-transfusion testing; Immunodiagnostics; Immunobiology; Complement in immunohaematology; Blood typing reagents; Genetic markers of blood cells and serum proteins: polymorphisms and function; Genetic markers and disease; Parentage testing and forensic immunohaematology. 5) Cellular Therapy: Cell-based therapies; Stem cell sources; Stem cell processing and storage; Stem cell products; Stem cell plasticity; Regenerative medicine with cells; Cellular immunotherapy; Molecular therapy; Gene therapy.