---
tags:
- image-classification
- microbiology
- gram-stain
- medical-imaging
- research-use-only
pipeline_tag: image-classification
---

# BiTTE-lite

<p align="center">
  <img src="./icon.jpg" alt="BiTTE-lite icon" width="220">
</p>

BiTTE-lite is an application within the CarbConnect platform. It is a simplified version of the BiTTE product, designed for easy and efficient detection of microorganisms in various applications.

The app focuses on classifying microorganisms from Gram-stained microscopy images into seven output categories:

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

## Intended Uses and Limitations

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

It is **not** intended for:

- clinical diagnostics
- medical decision-making
- patient management
- therapeutic selection
- any regulated medical procedure

For additional product details, refer to the BiTTE-lite 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 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-lite in research, please cite:

```bibtex
@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 detailed classification at the species level, refer to **BiTTE - iE**.

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