Real-World Deployment of an Ethical AI-Assisted Tele-Ultrasound Platform for Endometriosis Diagnosis in the Brazilian Public Health System: The EndoConnect Alpha Case Study and Its Evolution into the NAM-Endora Framework (Preprint)

Kelnner Portela Luz
2026 · doi:10.2196/preprints.89414 · W7160321845
preprint OA: gold CC0
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EndoConnect Alpha, an ethical AI-assisted tele-ultrasound platform for endometriosis diagnosis in Brazil’s public health system, demonstrated high usability and significant clinical benefits including pain reduction and improved therapy adherence.

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This preprint evaluates the EndoConnect Alpha platform, an ethical AI-assisted tele-ultrasound system deployed across ten primary care units in Brazil’s public health system. The study involved 60 participants and found that the platform achieved high usability scores, significant pain reduction, and improved therapy adherence among users. While the results demonstrate feasibility and impact in low-resource settings, the authors note that multicenter validation is still required to confirm these findings broadly. This paper is centrally about endometriosis — specifically focusing on diagnostic tools and digital health interventions designed to reduce the prolonged diagnostic delays associated with the condition in the Brazilian healthcare context.

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Abstract

BACKGROUND Endometriosis affects 10 % of reproductive-age women globally, with diagnostic delays of 7–10 years in Brazil’s SUS. Digital platforms offer promise, but few are ethically designed with AI for low-resource settings. OBJECTIVE To report real-world deployment of EndoConnect Alpha in SUS primary care, evaluate usability/acceptability, and describe its evolution into the NAM-Endora ethical AI framework. METHODS Applied methodological study. EndoConnect Alpha (React.js + Firebase) deployed in 10 SUS units (Ceará). n = 60 (45 patients, 15 professionals). Instruments: SUS, TAM, engagement metrics, clinical-psychosocial outcomes. Ethics CAAE 82094924.8.0000.5049. INPI BR5120250005556-0. RESULTS SUS 88.9 ± 9.8 (excellent); TAM 91.4.57/5. Trail completion 79 %. Pain reduction 23 % (VAS p=0.02), therapy adherence +17 %, anxiety −14 %. Strong SUS-TAM correlation (ρ=0.76, p<0.001). CONCLUSIONS EndoConnect Alpha is feasible and impactful in SUS. NAM-Endora provides scalable ethical AI governance for LMICs. Multicenter validation planned. CLINICALTRIAL Not applicable (formative research)
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Abstract

Background: Endometriosis affects 10 % of reproductive-age women globally, with diagnostic delays of 7–10 years in Brazil’s SUS. Digital platforms offer promise, but few are ethically designed with AI for low-resource settings.

Objective

To report real-world deployment of EndoConnect Alpha in SUS primary care, evaluate usability/acceptability, and describe its evolution into the NAM-Endora ethical AI framework.

Methods

Applied methodological study. EndoConnect Alpha (React.js + Firebase) deployed in 10 SUS units (Ceará). n = 60 (45 patients, 15 professionals). Instruments: SUS, TAM, engagement metrics, clinical-psychosocial outcomes. Ethics CAAE 82094924.8.0000.5049. INPI BR5120250005556-0.

Results

SUS 88.9 ± 9.8 (excellent); TAM 91.4.57/5. Trail completion 79 %. Pain reduction 23 % (VAS p=0.02), therapy adherence +17 %, anxiety −14 %. Strong SUS-TAM correlation (ρ=0.76, p<0.001).

Conclusions

EndoConnect Alpha is feasible and impactful in SUS. NAM-Endora provides scalable ethical AI governance for LMICs. Multicenter validation planned. Clinical Trial: Not applicable (formative research) Citation Request queued. Please wait while the file is being generated. It may take some time. Copyright © The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

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Outcome instruments

VAS-pain

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endometriosis

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