International Journal on Science and Technology

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Use of Artificial Intelligence for Automated Recognition of Uncommon Pathogens in a Clinical Laboratory Setting: A Case Study

Author(s) Sultan A. Aldhabi, Abdullah D. Tawhari, Suliman D. Alnuais, Maha H. Alenazi, Israa A. Alqurayqiri, Abeer K. Al-Agily, Kholood M. AlSahli
Country Saudi Arabia
Abstract Intoduction: AI systems will transform clinical laboratories by improving diagnostic precision, shortening TAT, and identifying uncommon pathogens. This research investigates the diagnostic AI integration in a clinical laboratory at a Riyadh tertiary hospital.
Materials and Methods: A 1000 set of samples containing various biological tissues such as blood, sputum, cerebrospinal fluid, and tissue biopsies were used for an observational prospective study. The integration of the AI algorithms together with the NGS data enabled the identification of rare pathogens. The diagnostic efficiency, TAT and rare pathogen identification were evaluated in both the conventional and AI_ Assisted work flows. Their opinions were qualitatively analyzed regarding the use of AI.
Results: In comparison with the conventional methods, where the p- value was less than 0.001, the AI system had an overall sensitivity of 97.3% and specificity of 95.8%. AI cut the average search TAT by 40%, and discovered approximately 41.7 % of rare pathogens. In qualitative analysis, user satisfaction was high, but problems with data quality and the initial costs were mentioned.
Summary: AI has the potential to change diagnostic processes in laboratories, which significantly increases diagnostic ability, TAT, and lowest detection rate of rare pathogens. There are unresolved questions concerning data quality and cost issues.
Keywords AI, Pathogen Identification, Clinical Testing, Reporting Timeliness, Next Generation Sequencing, AI in Healthcare, Diagnostics Centers
Published In Volume 15, Issue 3, July-September 2024
Published On 2024-09-04
Cite This Use of Artificial Intelligence for Automated Recognition of Uncommon Pathogens in a Clinical Laboratory Setting: A Case Study - Sultan A. Aldhabi, Abdullah D. Tawhari, Suliman D. Alnuais, Maha H. Alenazi, Israa A. Alqurayqiri, Abeer K. Al-Agily, Kholood M. AlSahli - IJSAT Volume 15, Issue 3, July-September 2024. DOI 10.5281/zenodo.14555225
DOI https://doi.org/10.5281/zenodo.14555225
Short DOI https://doi.org/g8wxhn
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