pocketgull-lateral-flow-edge

PocketGull Edge Lateral Flow & Biomarker Vision Scanner

Organization: PocketGull LLC (Oregon Registry: 258869891)
Informatics Lead: Phillip Gear (CMS NPI: 1487569752 | ORCID: 0009-0008-1372-5381)
Base Foundation Model: pocketgull/edge-densitometry-vision
Discipline: Zero-Cloud Computer Vision Densitometry for Rapid Antigen & Salivary Strips
Open Science Provenance: Zenodo DOI 10.5281/zenodo.20647514

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📌 Overview

Low-cost point-of-care edge ML model that reads and quantifies rapid lateral flow immunoassay cassettes and salivary biomarker strips directly on smartphone cameras. Verifies control line validity (>0.18 OD), quantifies test-to-control optical density ratios, and outputs structured HL7 FHIR R4 Observation bundles (LOINC 94558-4) with zero cloud egress.

This LoRA adapter was fine-tuned using Direct Preference Optimization (DPO) on domain-specific clinical datasets conforming strictly to HIPAA §164.514 Safe Harbor de-identification standards.

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🚀 Quickstart Inference (Transformers & PEFT)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model_id = "pocketgull/edge-densitometry-vision"
adapter_id = "pocketgull-llc/pocketgull-lateral-flow-edge"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)

prompt = "Patient presents with palpitations taking St. John's Wort alongside Warfarin. Evaluate CYP450 metabolism."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.2)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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🔒 HIPAA & Regulatory Compliance

• Zero-PHI Retention: Designed for local edge computation and private Google Cloud Vertex AI deployment.
• FDA 520(o) Non-Device CDS: Supportive evidence-grounded tool intended to assist licensed healthcare providers.

📖 Citation

@software{pocketgull_clinical_2026,
  author = {Gear, Phillip},
  title = {Pocket-Gull: Living Medical Intelligence Engine & Open Clinical Science Suite},
  publisher = {Zenodo},
  version = {1.25.0},
  year = {2026},
  doi = {10.5281/zenodo.20647514},
  url = {https://pocketgull.app}
}