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ARXIV:2603.26351 · MEDICAL AI · SUBMITTED 30 MAR · 21:53 UTC · FRESHNESS STALE
ARXIV:2603.26351MEDICAL AISUBMITTED 30 MAR · 21:53 UTCFRESHNESS STALEQurat Ul Ain · Alptekin Temizel · Soyiba Jawed · arXiv
An interpretable AI framework for ADHD classification using structural MRI, identifying key brain regions as potential biomarkers.
Opportunity summary
Pain An interpretable AI framework for ADHD classification using structural MRI, identifying key brain regions as potential biomarkers.
Evidence 26 refs | 3 sources | 50% coverage
Blocker Evidence unverified
An interpretable AI framework for ADHD classification using structural MRI, identifying key brain regions as potential biomarkers. Structural MRI (sMRI) provides a non-invasive modality for investigating brain alterations associated with ADHD; nevertheless, most deep…
Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition; however, its neurobiological diagnosis remains challenging due to the lack of reliable imaging-based biomarkers, particularly anatomical markers. Structural MRI (sMRI) provides a non-invasive…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. DuSCN-FusionNet further achieves precision, recall, and F1-scores of 81.66%, 80.59%, and 80.27%, respectively. Code availability is flagged in the production record; the public repository…
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
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An interpretable AI framework for ADHD classification using structural MRI, identifying key brain regions as potential biomarkers.
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10.48550/arXiv.2603.26351An interpretable AI framework for ADHD classification using structural MRI, identifying key brain regions as potential biomarkers.
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition; however, its neurobiological diagnosis remains challenging due to the lack of reliable imaging-based biomarkers, particularly anatomical markers. Structural MRI (sMRI) provides a non-invasive modality for investigating brain alterations associated with ADHD; nevertheless, most deep learning approaches function as black-box systems, limiting clinical trust and interpretability. In this work, we propose DuSCN-FusionNet, an interpretable sMRI-based framework for ADHD classification that leverages dual-channel Structural Covariance Networks (SCNs) to capture inter-regional morphological relationships. ROI-wise mean intensity and intra-regional variability descriptors are used to construct intensity-based and heterogeneity-based SCNs, which are processed through an SCN-CNN encoder. In parallel, auxiliary ROI-wise variability features and global statistical descriptors are integrated via late-stage fusion to enhance performance. The model is evaluated using stratified 10-fold cross-validation with a 5-seed ensemble strategy, achieving a mean balanced accuracy of 80.59% and an AUC of 0.778 on the Peking University site of the ADHD-200 dataset. DuSCN-FusionNet further achieves precision, recall, and F1-scores of 81.66%, 80.59%, and 80.27%, respectively. Moreover, Grad-CAM is adapted to the SCN domain to derive ROI-level importance scores, enabling the identification of structurally relevant brain regions as potential biomarkers.
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Proof status
unverified26 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 7.0
PROBLEM
An interpretable AI framework for ADHD classification using structural MRI, identifying key brain regions as potential biomarkers. Structural MRI (sMRI) provides a non-invasive modality for investigating brain alterations associated with ADHD; nevertheless, most deep learning ap...
METHOD
Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition; however, its neurobiological diagnosis remains challenging due to the lack of reliable imaging-based biomarkers, particularly anatomical markers. Structural MRI (sMRI) provides a...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. DuSCN-FusionNet further achieves precision, recall, and F1-scores of 81.66%, 80.59%, and 80.27%, respectively. Code availability is flagged in the production record; the public repository link still needs...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
achieving a mean balanced accuracy of 80.59%
The abstract explicitly states this performance metric.
partial
and an AUC of 0.778
The abstract explicitly states this performance metric.
partial
achieving a mean balanced accuracy of 80.59%
The abstract explicitly states this performance metric.
partial
and an AUC of 0.778
The abstract explicitly states this performance metric.
partial
leverages dual-channel Structural Covariance Networks (SCNs) to capture inter-regional morphological relationships.
This is a core methodological description in the abstract.
partial
ROI-wise mean intensity and intra-regional variability descriptors are used to construct intensity-based and heterogeneity-based SCNs
This details the construction of the SCNs, a key methodological aspect.
partial
auxiliary ROI-wise variability features and global statistical descriptors are integrated via late-stage fusion
This describes a specific fusion strategy used in the method.
partial
Grad-CAM is adapted to the SCN domain to derive ROI-level importance scores
This describes the interpretability method used.
partial
The model is evaluated using stratified 10-fold cross-validation with a 5-seed ensemble strategy
This details the experimental setup for evaluation.
partial
DuSCN-FusionNet further achieves precision, recall, and F1-scores of 81.66%, 80.59%, and 80.27%, respectively.
These specific performance metrics are explicitly stated in the abstract.
partial
leverages dual-channel Structural Covariance Networks (SCNs) to capture inter-regional morphological relationships.
This is a core methodological description in the abstract.
partial
ROI-wise mean intensity and intra-regional variability descriptors are used to construct intensity-based and heterogeneity-based SCNs
This details the specific inputs for the SCNs, as described in the abstract.
partial
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An interpretable AI framework for ADHD classification using structural MRI, identifying key brain regions as potential biomarkers.
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Medical AI
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Commercial read
7.0/10 public viability
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status
missing
reason
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proof status
unverified
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confidence low
next verification path
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Evidence coverage
OpportunityKernel evidence_receipt
26 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
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
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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partial
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Research evidence exists; buyer urgency still needs source proof.
Evidence
26 references, 3 sources, 50% evidence coverage.
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missing
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No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
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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
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