Add BiTTE-lite model card

README.md

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+---
+tags:
+- image-classification
+- microbiology
+- gram-stain
+- medical-imaging
+- research-use-only
+pipeline_tag: image-classification
+---
+
+# BiTTE-lite
+
+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)**.