CLINICAL DIAGNOSIS THE NEGATIVE CONSEQUENCES OF DIAGNOSTIC ERRORS IN LABORATORY PRACTICE AND THE RELEVANCE OF THEIR ELIMINATION
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Аннотация:
One prominent form of medical error and avoidable iatrogenic injury is diagnostic error, which includes missed, delayed, or incorrect diagnosis. Diagnostic errors may result from mistakes made during the laboratory testing procedure. The purpose of this retrospective study of voluntary event reports was to look into the types, reasons, and clinical effects of errors—including diagnostic errors—that occur during clinical laboratory testing. A number of papers published in the past 20 years have brought laboratory professionals' attention to the pre- and post-analytical phases, which currently seem to be more susceptible to errors than the analytical phase. This is true even though the frequency of laboratory errors varies greatly, depending on the study design and steps of the entire testing process investigated.
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van Moll C, Egberts T, Wagner C, Zwaan L, Ten Berg M. The Nature, Causes, and Clinical Impact of Errors in the Clinical Laboratory Testing Process Leading to Diagnostic Error: A Voluntary Incident Report Analysis. J Patient Saf. 2023 Dec 1;19(8):573-579. doi: 10.1097/PTS.0000000000001166.
Plebani M. Diagnostic Errors and Laboratory Medicine - Causes and Strategies. EJIFCC. 2015 Jan 27;26(1):7-14.
Shapiro HM, Apte SH, Chojnowski GM, Hänscheid T, Rebelo M, Grimberg BT. Cytometry in malaria—a practical replacement for microscopy? Curr Protoc Cytom. 2013;chapt 11:Unit 11.20 doi:10.1002/0471142956.cy1120s65.
WHO. World Malaria Report. Geneva: World Health Organization; 2014.
Baum E, Sattabongkot J, Sirichaisinthop J, Kiattibutr K, Davies DH, Jain A, et al. Submicroscopic and asymptomatic Plasmodium falciparum and Plasmodium vivax infections are common in western Thailand—molecular and serological evidence. Malar J. 2015;14:95.
WHO. Malaria. Geneva: World Health Organization. 2015. http://www.who.int/ith/diseases/malaria/en/. Accessed 20 May 2015.
Ahamed, M., Nahiduzzaman, M., Mahmud, G. et al. Improving Malaria diagnosis through interpretable customized CNNs architectures. Sci Rep 15, 6484 (2025). https://doi.org/10.1038/s41598-025-90851-1
Li, S., Li, T., Sun, C., Yan, R. & Chen, X. Multilayer Grad-CAM: An effective tool towards explainable deep neural networks for intelligent fault diagnosis. J. Manuf. Syst. 69, 20–30 (2023).
Panwar, H. et al. A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images. Chaos Solitons Fractals 140, 110190 (2020).
Zhang, J. et al. Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model. J. Environ. Manage 332, 117357 (2023).
Li, Z. Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Comput. Environ. Urban Syst. 96, 101845 (2022).
Ahamed, Md. F., Shafi, F. B., Nahiduzzaman, Md., Ayari, M. A. & Khandakar, A. Interpretable deep learning architecture for gastrointestinal disease detection: A Tri-stage approach with PCA and XAI. Comput. Biol. Med. 185, 109503 (2025).
Faruq Goni, M. O. & Islam Mondal, M. N. Explainable AI Based Malaria Detection Using Lightweight CNN. 2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023, https://doi.org/10.1109/NCIM59001.2023.10212621. (2023).
Faysal Ahamed, M. et al. Automated Colorectal Polyps Detection from Endoscopic Images using MultiResUNet Framework with Attention Guided Segmentation. Human-Centric Intelligent Systems 2024 4:2 4, 299–315 (2024).
Raihan, M. J. & Nahid, A. Al. Malaria cell image classification by explainable artificial intelligence. Health Technol (Berl) 12, 47–58 (2022).
Devi, S. S., Roy, A., Singha, J., Sheikh, S. A. & Laskar, R. H. Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear. Multimed. Tools Appl. 77, 631–660 (2018).
Alonso-Ramirez, A. A. et al. Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks. IEEE Access 10, 97348–97359 (2022).
Ha, Y., Meng, X., Du, Z., Tian, J. & Yuan, Y. Semi-supervised graph learning framework for apicomplexan parasite classification. Biomed. Signal Process Control https://doi.org/10.1016/j.bspc.2022.104502 (2023).
Ahamed, Md. F. et al. Interpretable Deep Learning Model for Tuberculosis Detection Using X-Ray Images. Surveillance, Prevention, and Control of Infectious Diseases 169–192 https://doi.org/10.1007/978-3-031-59967-5_8. (2024).
Chakradeo, K., Delves, M. & Titarenko, S. Malaria parasite detection using deep learning methods. Int. J. Comput. Inf. Eng. 15, 175–182 (2021)
Goni, M. O. F. et al. Diagnosis of Malaria using double hidden layer extreme learning machine algorithm with CNN feature extraction and parasite inflator. IEEE Access 11, 4117–4130 (2023).
Nundu, S. S. et al. Malaria parasite species composition of Plasmodium infections among asymptomatic and symptomatic school-age children in rural and urban areas of Kinshasa. Democratic Republ. Congo. Malar J. https://doi.org/10.1186/s12936-021-03919-4 (2021).