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Filter

A SpineDao Project

An AI-powered triage tool that analyzes patient data to prioritize cases and optimize treatment pathways, integrating expert input and token incentives for data sharing.

90% of patients at spine surgery offices being non-surgical candidates is an unsustainable burden for all.

What is FILTER?

FILTER is an AI tool powered by a chatbot named Lamina, designed to streamline patient triage in spine care. By analyzing patient data, such as clinical notes, and imaging, etc., FILTER identifies appropriate treatment pathways and prioritizes cases, reducing the workload for healthcare providers. It ensures patients get the right treatment at the right time.

Key Goals

The primary goal of FILTER is to improve spine care efficiency as follows:

  • Offer timely and appropriate spine care pathways (conservative treatment, minimal intervention or surgical expertise) to patients seeking treatment.
  • Decrease financial burden for patients and healthcare payers by reducing unnecessary consultations.
  • Introduce token-incentivized, multi-labeled data to capture both surgical and medical expertise.

How does FILTER work?

  1. Data Collection and Multi-Labeled Annotation: FILTER gathers and labels patient data, such as MRI/CT scans, clinical notes, and other medical records, by using multiple labels to capture more detailed information about each patient.
  2. AI-Powered Analysis: FILTER uses advanced machine learning to examine multi-labeled data and classify and prioritize patient cases by urgency and complexity. In addition, integrating the input of spine doctors and surgeons ensures that each case is assessed in a manner that fits each unique need.
  3. Dual Incentive System: Both the patients and clinicians were rewarded for their contributions. Patients earn incentives to share their medical data, whereas providers get rewarded for reviewing and verifying case information. This system ensures high-quality data and expert input, thereby benefiting everyone involved.

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Roadmap

Data Collection & Preparation

  • Aggregate surgical datasets from collaborating surgeons.
  • Ensure comprehensive data, including clinical profiles, MRI/CT scans, surgical strategies, and outcomes.
  • Standardize dataset labeling templates for consistency.

AI Model Development & Training

  • Train AI models to predict surgical outcomes and optimize decision-making.
  • Validate predictions through prospective analysis.
  • Refine algorithms for maximum accuracy.

Beta Testing with Practitioners

  • Deploy beta models with selected SRC surgeons for real-world validation.
  • Gather feedback on decision-support accuracy and usability.
  • Optimize AI models based on clinical input.

Commercialization & Deployment

  • Finalize AI-assisted surgical decision tool.
  • Establish partnerships with hospitals, insurers, and health tech platforms.
  • Launch licensing agreements for DeScide AI solutions.