AI Translates Multi-Test Signals into Clinician-Ready SIBO Reports

AI-Powered Diagnostics for SIBO Detection

Executive Summary

The product and AI engineering team delivered an end-to-end diagnostic platform for detecting Small Intestinal Bacterial Overgrowth (SIBO). The system coordinates kit fulfillment, captures patient intake and test data, applies supervised models to estimate risk categories, and generates clinician-ready, standardized result sheets. A privacy-by-design architecture and a continuous learning loop enable the platform to improve as more cases flow through, while keeping clinicians in control of final interpretation.

Problem

Grading written narratives is to education what interpreting multi-signal diagnostics is to healthcare: necessary but slow, and often inconsistent without structure. SIBO evaluation requires integrating signals from different test modalities, yet training data can be sparse at launch. Diagnostic teams needed a scalable workflow that: ingests diverse test outputs, produces reliable category predictions despite limited data, generates clear explanations and structured reports, and progressively gets better—without requiring wholesale changes to clinical routines.

Solution

The platform was engineered around four pillars. First, remote testing and intake: kit logistics, consented patient onboarding, and guided data capture across multiple test types with validation and error checks. Second, ML scoring: supervised models with feature pipelines tailored to each modality, probabilistic calibration for thresholding, and explainability artifacts that highlight contributing signals. Third, reporting and wayfinding: clinician-ready PDFs and patient summaries with standardized ranges, risk bands, and next-step guidance language approved by medical leads (decision support, not diagnosis). Fourth, learning and governance: an auto-training loop that incorporates new labeled cases under human review, model registry and versioning, performance dashboards, and drift monitoring to protect against silent degradation. Security controls—RBAC, encryption in transit/at rest, audit trails—anchor compliance expectations.

Outcome

Diagnostic operations move from manual aggregation to a consistent, explainable pipeline. Clinicians receive structured reports with transparent risk categories; patients get understandable summaries. As volume grows, the model improves under governance, expanding support for additional test types without re-architecting the platform. The net effect is faster turnaround, fewer ambiguities in interpretation, and a durable foundation for remote diagnostics.

What You Can Expect Working with Dreamloop Studio

Dreamloop Studio’s product and AI teams design clinical-grade data flows that respect privacy and clinician judgment. Expect disciplined ingestion, calibrated models with explainability, and reporting that reduces rework. The deliverable is not just a model, but a governed diagnostic capability that scales with your test catalog.