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ARXIV:2603.06885 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.06885MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
We provide an open-source, preprocessed trachoma eye dataset (OPTED) and code to enable automated trachoma classification, addressing a critical need in Sub-Saharan Africa.
Opportunity summary
Pain We provide an open-source, preprocessed trachoma eye dataset (OPTED) and code to enable automated trachoma classification, addressing a critical need in Sub-Saharan Africa.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
We provide an open-source, preprocessed trachoma eye dataset (OPTED) and code to enable automated trachoma classification, addressing a critical need in Sub-Saharan Africa. Yet publicly available preprocessed datasets for automated trachoma classification are scarce,…
Trachoma remains the leading infectious cause of blindness worldwide, with Sub-Saharan Africa bearing over 85% of the global burden and Ethiopia alone accounting for more than half of all cases. Yet publicly available preprocessed…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The OPTED dataset, preprocessing code, and all experimental artifacts are released as open source to facilitate reproducible trachoma classification research.
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
We provide an open-source, preprocessed trachoma eye dataset (OPTED) and code to enable automated trachoma classification, addressing a critical need in Sub-Saharan Africa.
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10.48550/arXiv.2603.06885We provide an open-source, preprocessed trachoma eye dataset (OPTED) and code to enable automated trachoma classification, addressing a critical need in Sub-Saharan Africa.
Abstract
Trachoma remains the leading infectious cause of blindness worldwide, with Sub-Saharan Africa bearing over 85% of the global burden and Ethiopia alone accounting for more than half of all cases. Yet publicly available preprocessed datasets for automated trachoma classification are scarce, and none originate from the most affected region. Raw clinical photographs of eyelids contain significant background noise that hinders direct use in machine learning pipelines. We present OPTED, an open-source preprocessed trachoma eye dataset constructed using the Segment Anything Model 3 (SAM 3) for automated region-of-interest extraction. We describe a reproducible four-step pipeline: (1) text-prompt-based zero-shot segmentation of the tarsal conjunctiva using SAM 3, (2) background removal and bounding-box cropping with alignment, (3) quality filtering based on confidence scores, and (4) Lanczos resizing to 224x224 pixels. A separate prompt-selection stage identifies the optimal text prompt, and manual quality assurance verifies outputs. Through comparison of five candidate prompts on all 2,832 known-label images, we identify "inner surface of eyelid with red tissue" as optimal, achieving a mean confidence of 0.872 (std 0.070) and 99.5% detection rate (the remaining 13 images are recovered via fallback prompts). The pipeline produces outputs in two formats: cropped and aligned images preserving the original aspect ratio, and standardized 224x224 images ready for pre-trained architectures. The OPTED dataset, preprocessing code, and all experimental artifacts are released as open source to facilitate reproducible trachoma classification research.
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Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
We provide an open-source, preprocessed trachoma eye dataset (OPTED) and code to enable automated trachoma classification, addressing a critical need in Sub-Saharan Africa. Yet publicly available preprocessed datasets for automated trachoma classification are scarce, and none or...
METHOD
Trachoma remains the leading infectious cause of blindness worldwide, with Sub-Saharan Africa bearing over 85% of the global burden and Ethiopia alone accounting for more than half of all cases. Yet publicly available preprocessed datasets for automated trachoma classification a...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The OPTED dataset, preprocessing code, and all experimental artifacts are released as open source to facilitate reproducible trachoma classification research.
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
We provide an open-source, preprocessed trachoma eye dataset (OPTED) and code to enable automated trachoma classification, addressing a critical need in Sub-Saharan Africa. Yet publicly available preprocessed datasets for automated trachoma classification are scarce, and none originate from the most affected region.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Trachoma remains the leading infectious cause of blindness worldwide, with Sub-Saharan Africa bearing over 85% of the global burden and Ethiopia alone accounting for more than half of all cases. Yet publicly available preprocessed datasets for automated trachoma classification are scarce, and none originate from the most affected region.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The OPTED dataset, preprocessing code, and all experimental artifacts are released as open source to facilitate reproducible trachoma classification research.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
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We provide an open-source, preprocessed trachoma eye dataset (OPTED) and code to enable automated trachoma classification, addressing a critical need in Sub-Saharan Africa.
Segment
Medical AI
Adoption evidence
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Commercial read
7.0/10 public viability
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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