BiTTE

BiTTE is an application within the CarbConnect platform. It is designed for the simple and efficient classification of microorganisms from Gram-stained microscopy images.

The app classifies findings into seven primary groups:

1. Gram-negative rods (GNR)
2. Gram-negative cocci (GNC)
3. Gram-positive rods (GPR)
4. Gram-positive cocci (GPC)
5. Yeast-like fungi
6. No bacteria
7. Multiple bacteria

BiTTE also supports more detailed subcategories beyond these primary output groups.

Intended Uses and Limitations

BiTTE is strictly intended for Research Use Only (RUO).

It is not intended for:

• clinical diagnostics
• medical procedures
• patient management
• therapeutic selection
• any regulated clinical use

For further details, please refer to the BiTTE Learn More page on CarbConnect.

How to Use

A video tutorial demonstrating how to use the app is available on YouTube.

Training Data

The model was trained on a dataset of Gram-stained images of urine and blood culture specimens generously provided by:

• the School of Medicine, Kobe University
• the National Center for Global Health and Medicine (NCGM)

Specimens were Gram-stained using either the Favor or Barmy method.

Image acquisition was performed by photographing specimens through the eyepiece of an optical microscope at 1000x magnification using a smartphone camera.

The dataset captures frequently encountered clinical bacterial species and includes:

• 15 species in urine specimens
• 19 species in aerobic blood culture specimens
• 13 species in anaerobic blood culture specimens

Performance and Evidence

Related publication:

Kei Yamamoto, Goh Ohji, et al. Accuracy of classification of urinary Gram-stain findings by a computer-aided diagnosis app compared with microbiology specialists. J Med Microbiol. 2025 Apr;74(4):002008. doi: 10.1099/jmm.0.002008.

Paper link:

https://www.microbiologyresearch.org/content/journal/jmm/10.1099/jmm.0.002008

Citation

If you use BiTTE in research, please cite:

@article{yamamoto2025bitte,
  author = {Yamamoto, Kei and Ohji, Goh and others},
  title = {Accuracy of classification of urinary Gram-stain findings by a computer-aided diagnosis app compared with microbiology specialists},
  journal = {Journal of Medical Microbiology},
  year = {2025},
  month = {Apr},
  volume = {74},
  number = {4},
  pages = {002008},
  doi = {10.1099/jmm.0.002008}
}

Other Remarks

For guidance on achieving high-quality Gram staining, please refer to the automated gram stainer Point of Care Gram Stainer (PoCGS).