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ARXIV:2603.21566 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.21566MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEMohammad Eslami · Dhanvinkumar Ganeshkumar · Saber Kazeminasab · Michael G. Morley · Michael V. Boland · Michael M. Lin · +5 at arXiv
A domain-adapted AI model and annotation toolkit for real-time segmentation in ophthalmic surgery, enabling precise intraoperative perception and scalable dataset development.
Opportunity summary
Pain A domain-adapted AI model and annotation toolkit for real-time segmentation in ophthalmic surgery, enabling precise intraoperative perception and scalable dataset development.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A domain-adapted AI model and annotation toolkit for real-time segmentation in ophthalmic surgery, enabling precise intraoperative perception and scalable dataset development. Positioned at the intersection of computer vision and medical robotics, CataractSAM-2 enables precise…
We present CataractSAM-2, a domain-adapted extension of Meta's Segment Anything Model 2, designed for real-time semantic segmentation of cataract ophthalmic surgery videos with high accuracy. Positioned at the intersection of computer vision and medical…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Positioned at the intersection of computer vision and medical robotics, CataractSAM-2 enables precise intraoperative perception crucial for robotic-assisted and computer-guided surgical systems. Code availability…
Medical AI moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A domain-adapted AI model and annotation toolkit for real-time segmentation in ophthalmic surgery, enabling precise intraoperative perception and scalable dataset development.
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10.48550/arXiv.2603.21566A domain-adapted AI model and annotation toolkit for real-time segmentation in ophthalmic surgery, enabling precise intraoperative perception and scalable dataset development.
Abstract
We present CataractSAM-2, a domain-adapted extension of Meta's Segment Anything Model 2, designed for real-time semantic segmentation of cataract ophthalmic surgery videos with high accuracy. Positioned at the intersection of computer vision and medical robotics, CataractSAM-2 enables precise intraoperative perception crucial for robotic-assisted and computer-guided surgical systems. Furthermore, to alleviate the burden of manual labeling, we introduce an interactive annotation framework that combines sparse prompts with video-based mask propagation. This tool significantly reduces annotation time and facilitates the scalable creation of high-quality ground-truth masks, accelerating dataset development for ocular anterior segment surgeries. We also demonstrate the model's strong zero-shot generalization to glaucoma trabeculectomy procedures, confirming its cross-procedural utility and potential for broader surgical applications. The trained model and annotation toolkit are released as open-source resources, establishing CataractSAM-2 as a foundation for expanding anterior ophthalmic surgical datasets and advancing real-time AI-driven solutions in medical robotics, as well as surgical video understanding.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Viability
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Dimensions overall score 8.0
PROBLEM
A domain-adapted AI model and annotation toolkit for real-time segmentation in ophthalmic surgery, enabling precise intraoperative perception and scalable dataset development. Positioned at the intersection of computer vision and medical robotics, CataractSAM-2 enables precise i...
METHOD
We present CataractSAM-2, a domain-adapted extension of Meta's Segment Anything Model 2, designed for real-time semantic segmentation of cataract ophthalmic surgery videos with high accuracy. Positioned at the intersection of computer vision and medical robotics, CataractSAM-2 e...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Positioned at the intersection of computer vision and medical robotics, CataractSAM-2 enables precise intraoperative perception crucial for robotic-assisted and computer-guided surgical systems. Code avai...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
designed for real-time semantic segmentation of cataract ophthalmic surgery videos with high accuracy.
Explicitly stated in the abstract as a core capability of the model.
partial
This tool significantly reduces annotation time and facilitates the scalable creation of high-quality ground-truth masks.
Directly stated in the abstract as a key benefit of the introduced tool.
partial
We also demonstrate the model's strong zero-shot generalization to glaucoma trabeculectomy procedures.
Explicitly stated in the abstract as a demonstrated result.
partial
The trained model and annotation toolkit are released as open-source resources.
Explicitly and unambiguously stated in the abstract.
partial
Generalization to all anterior segment surgeries is assumed but should be tested further.
Directly stated in the analysis excerpt under 'caveats', indicating a recognized limitation.
partial
The method relies on high-quality input videos and specific prompts for accurate results.
Directly stated in the analysis excerpt under 'caveats', indicating a technical dependency.
partial
facilitates the scalable creation of high-quality ground-truth masks, accelerating dataset development for ocular anterior segment surgeries.
Directly stated in the abstract as a key function of the annotation framework.
partial
Availability of surgical video data may be limited by privacy concerns.
Directly stated in the analysis excerpt under 'caveats', indicating a practical constraint.
partial
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A domain-adapted AI model and annotation toolkit for real-time segmentation in ophthalmic surgery, enabling precise intraoperative perception and scalable dataset development.
Segment
Medical AI
Adoption evidence
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Commercial read
8.0/10 public viability
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status
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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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Buyer clarity
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Defensibility
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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.
Capital intensity
missing
Current read
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missing
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
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Operator workflow not sourced.
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Regulatory need unclassified.
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Gaps
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ARTIFACTS
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DEFENSIBILITY
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