Use of artificial intelligence for tailored routine urine analyses
| Autor: | Frédéric Laurent, Christine Fuhrmann, Gerard Lina, Jean François Sauzon, Cécile Eymard, Laura Chanel, Anatole Luzzati, Kevin Santos, François Vandenesch, Pascale Girardo, Marc Guillaumont, Olivier Dauwalder, Pablo Roy-Azcora, Chantal Sobas, Agathe Michel |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Microbiology (medical) medicine.medical_specialty 030106 microbiology Urine Urinalysis Routine practice 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Internal medicine Humans Medicine 030212 general & internal medicine business.industry Reproducibility of Results Artificial intelligence software General Medicine Confidence interval Clinical microbiology Infectious Diseases Artificial bladder business Algorithms Software |
| Zdroj: | Clinical Microbiology and Infection. 27:1168.e1-1168.e6 |
| ISSN: | 1198-743X |
| Popis: | Objectives Urine is the most common material tested in clinical microbiology laboratories. Automated analysis is already performed, permitting quicker results and decreasing the laboratory technologist's (LT) workload. These automatic systems have introduced digital imaging concepts. PhenoMATRIX (PHM) is an artificial intelligence software that merges picture algorithms and user rules to provide presumptive results. This study aimed at designing a tailored workflow using PHM, performing its validation and checking its performance in routine practice. Methods Two data collections including 96 and 135 urine samples from nephrostomy/ureterostomy and artificial bladder (US), 948 and 1257 urine samples from catheter (UC) and 3251 and 2027 midstream urine (MSU) were used to compare LT results with those obtained using two versions of PHM. Another 19 US, 102 UC and 508 MSU were used to monitor performance level 3 months after routine implementation. Results Before and after revisions, agreement between the first version of PHM and LT results were 83% (95% confidence interval [CI], 74.3–90.2) and 83% (95% CI, 75.3–90.9) (US), 66.7% (95% CI, 63.5–69.5) and 71.7% (95% CI, 68.8–74.4) (UC) and 65.4% (95% CI, 63.8–67.1) and 76% (95% CI, 74.1–77.1) (MSU). The second version improved results, demonstrating 96.2% (95% CI, 91.6–98.8) and 97% (95% CI, 92.6–99.2) (US), 87.5% (95% CI, 85.5–89.2) and 88.9% (95% CI, 87.0–90.5) (UC) and 91% (95% CI, 89.7–92.1) and 92% (95% CI, 91.1–93.4) (MSU) of agreement with LT results before and after revisions. The reliability of PHM results was confirmed by a routine study demonstrating 92% (95% CI, 90.0–94.2) overall agreement. Conclusions PHM showed high performance, with >90% of results in agreement with LT. PHM could help standardize and secure results, prioritize positive plates during analytical workflow and likely save LT time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|