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ARXIV:2603.26400 · MEDICAL AI · SUBMITTED 30 MAR · 21:52 UTC · FRESHNESS STALE
ARXIV:2603.26400MEDICAL AISUBMITTED 30 MAR · 21:52 UTCFRESHNESS STALELe Ma · Thiago Freitas dos Santos · Nadia Magnenat-Thalmann · Katarzyna Wac · arXiv
A new multi-view dataset and benchmark for surgical hand-gesture and error recognition aims to automate medical training assessment, addressing scalability and cost limitations.
Opportunity summary
Pain A new multi-view dataset and benchmark for surgical hand-gesture and error recognition aims to automate medical training assessment, addressing scalability and cost limitations.
Evidence 73 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A new multi-view dataset and benchmark for surgical hand-gesture and error recognition aims to automate medical training assessment, addressing scalability and cost limitations. Automated AI-based assessment offers a viable alternative, but progress is constrained…
In surgical training for medical students, proficiency development relies on expert-led skill assessment, which is costly, time-limited, difficult to scale, and its expertise remains confined to institutions with available specialists. Automated AI-based assessment offers…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. SHands is publicly released to support the development of robust and scalable AI systems for surgical training grounded in clinically curated domain knowledge. Code…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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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
A new multi-view dataset and benchmark for surgical hand-gesture and error recognition aims to automate medical training assessment, addressing scalability and cost limitations.
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Paper Pack
10.48550/arXiv.2603.26400A new multi-view dataset and benchmark for surgical hand-gesture and error recognition aims to automate medical training assessment, addressing scalability and cost limitations.
Abstract
In surgical training for medical students, proficiency development relies on expert-led skill assessment, which is costly, time-limited, difficult to scale, and its expertise remains confined to institutions with available specialists. Automated AI-based assessment offers a viable alternative, but progress is constrained by the lack of datasets containing realistic trainee errors and the multi-view variability needed to train robust computer vision approaches. To address this gap, we present Surgical-Hands (SHands), a large-scale multi-view video dataset for surgical hand-gesture and error recognition for medical training. \textsc{SHands} captures linear incision and suturing using five RGB cameras from complementary viewpoints, performed by 52 participants (20 experts and 32 trainees), each completing three standardized trials per procedure. The videos are annotated at the frame level with 15 gesture primitives and include a validated taxonomy of 8 trainee error types, enabling both gesture recognition and error detection. We further define standardized evaluation protocols for single-view, multi-view, and cross-view generalization, and benchmark state-of-the-art deep learning models on the dataset. SHands is publicly released to support the development of robust and scalable AI systems for surgical training grounded in clinically curated domain knowledge.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run pending anchorsA parse run id is attached, but no public source anchors are materialized yet.
Proof status
unverified73 refs; 3 sources; 50% 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
A new multi-view dataset and benchmark for surgical hand-gesture and error recognition aims to automate medical training assessment, addressing scalability and cost limitations. Automated AI-based assessment offers a viable alternative, but progress is constrained by the lack of...
METHOD
In surgical training for medical students, proficiency development relies on expert-led skill assessment, which is costly, time-limited, difficult to scale, and its expertise remains confined to institutions with available specialists. Automated AI-based assessment offers a viab...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. SHands is publicly released to support the development of robust and scalable AI systems for surgical training grounded in clinically curated domain knowledge. Code availability is flagged in the producti...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Our dataset is the first to provide synchronized multi-view open hand surgery videos with comprehensive gesture and error annotations.
The abstract and Table 1 explicitly state this novelty.
partial
SHands captures linear incision and suturing using five RGB cameras from complementary viewpoints
The abstract and Figure 1 describe the multi-view camera setup.
partial
The videos are annotated at the frame level with 15 gesture primitives and include a validated taxonomy of 8 trainee error types
The abstract and Section 3 detail the annotation taxonomy.
partial
performed by 52 participants (20 experts and 32 trainees), each completing three standardized trials per procedure.
The abstract and Table 1 specify the participant numbers and trial structure.
partial
JIGSAWS [8] provide synchronized video and kinematic streams but fail to capture the manual hand–tool coordination central to open surgical training
The paper contrasts SHands with JIGSAWS, highlighting this limitation.
partial
Endoscopic phase datasets, such as Cholec80 [9] and M2CAI16 [10], offer single-view laparoscopic recordings but lack the fine-grained gesture boundaries and clinically validated error labels needed for targeted feedback.
The paper contrasts SHands with Cholec80, highlighting these limitations.
partial
We further define standardized evaluation protocols for single-view, multi-view, and cross-view generalization
The abstract explicitly mentions the defined evaluation protocols.
partial
The videos are annotated at the frame level with 15 gesture primitives and include a validated taxonomy of 8 trainee error types
The abstract and Section 3 detail the annotation taxonomy.
partial
Our dataset is the first to provide synchronized multi-view open hand surgery videos with comprehensive gesture and error annotations.
The abstract and Table 1 explicitly state this novelty.
partial
SHands captures linear incision and suturing using five RGB cameras from complementary viewpoints
The abstract and Figure 1 describe the multi-view camera setup.
partial
performed by 52 participants (20 experts and 32 trainees), each completing three standardized trials per procedure.
The abstract provides the participant numbers and trial details.
partial
enabling both gesture recognition and error detection.
The abstract states the purpose of the annotations.
partial
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Concepts
Methods
Materials
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Competitors
A new multi-view dataset and benchmark for surgical hand-gesture and error recognition aims to automate medical training assessment, addressing scalability and cost limitations.
Segment
Medical AI
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Commercially relevant
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3/3 checks · 100%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
73 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
Evidence
73 references, 3 sources, 50% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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