Yoshifumi Uwamino, Tatsuya Yamada, Go Yamamoto, Wataru Aoki, Rika Inose, Yuka Kamoshita, Akiko Ueda, Kanon Uehara, Haruna Taguchi, Takahisa Ishikawa, Yui Shigeishi, Yasuhide Kawamoto, Yu Hiraoka, Kosuke Kosai, Satoshi Kutsuna, Katsunori Yanagihara, Hiromichi Matsushita
The automated Gram staining system demonstrated high sensitivity but moderate specificity. Although its diagnostic performance remained inferior to experienced technologists, it enabled a fully automated workflow integrating staining, microscopy, image acquisition, and AI-assisted interpretation. These findings support the feasibility of integrated Gram stain workflow automation and provide a benchmark for further refinement of automated microscopy and AI algorithms.
BACKGROUND: Fully automated Gram staining systems integrating staining, microscopy, image acquisition, and artificial intelligence (AI)-assisted interpretation have the potential to reduce laboratory workload and standardize workflows. However, their diagnostic performance in routine clinical practice remains unclear. We evaluated a fully automated Gram staining system using clinical urine specimens from multiple institutions.
METHODS: In this prospective multicenter study, residual urine specimens from two Japanese university hospitals were evaluated using Mycrium, a fully automated system integrating Gram staining, microscopy, image acquisition, and AI-assisted interpretation. The standalone performance of the initial and updated AI models and the performance of manual microscopy were compared with a consensus reference standard established by three experienced clinical microbiology technologists.
RESULTS: Among 892 collected specimens, 882 were eligible for analysis. Manual microscopy demonstrated a sensitivity of 93.4%, specificity of 89.9%, and Cohen's κ of 0.83 for overall Gram stain interpretation. The initial AI model achieved a sensitivity of 91.0%, specificity of 49.3%, and κ of 0.41, whereas the updated model showed a sensitivity of 88.3%, specificity of 59.0%, and κ of 0.48. Model updating resulted in category-specific trade-offs between sensitivity and specificity. Most false-negative results occurred in 1+ specimens.
CONCLUSIONS: The automated Gram staining system demonstrated high sensitivity but moderate specificity. Although its diagnostic performance remained inferior to experienced technologists, it enabled a fully automated workflow integrating staining, microscopy, image acquisition, and AI-assisted interpretation. These findings support the feasibility of integrated Gram stain workflow automation and provide a benchmark for further refinement of automated microscopy and AI algorithms.