Date: 2025-11-30 Source Documents: DD-RAPTOR (n=50) + 2025 Literature (n=45)
| Study/Model | Disorder | Modality | Sample Size | Sensitivity | Specificity | AUC | Accuracy | 95% CI | Year | GRADE |
|---|---|---|---|---|---|---|---|---|---|---|
| Meta-Analysis (Deep Learning) | ASD | Mixed | n=9,495 (11 studies) | 0.95 | 0.93 | 0.98 | - | Sens: 0.88-0.98 Spec: 0.85-0.97 AUC: 0.97-0.99 |
2024 | ⊕⊕⊕○ MODERATE |
| Canvas Dx (Real-World) | ASD | Clinical/Behavioral | n=254 | 0.991 | 0.816 | - | - | Sens: 0.973-1.00 Spec: 0.708-0.925 |
2025 | ⊕⊕⊕○ MODERATE |
| SVM (Children) | ASD | Clinical | NR | - | - | - | 1.00 | NR | 2024 | ⊕⊕○○ LOW |
| Logistic Regression (Children) | ASD | Clinical | NR | - | - | - | 1.00 | NR | 2024 | ⊕⊕○○ LOW |
| Logistic Regression (Adults) | ASD | Clinical | NR | - | - | - | 0.9714 | NR | 2024 | ⊕⊕○○ LOW |
| SVM (ASD + ID) | ASD + Intellectual Disability | Clinical | NR | - | - | 0.829 | 0.836 | AUC: 0.738-0.920 | 2024 | ⊕⊕○○ LOW |
| Logistic Regression (ASD + ID) | ASD + Intellectual Disability | Clinical | NR | 0.939 | - | 0.858 | - | AUC: 0.770-0.944 | 2024 | ⊕⊕○○ LOW |
| Random Forest (ASD + ID) | ASD + Intellectual Disability | Clinical | NR | - | - | 0.845 | - | AUC: 0.747-0.944 | 2024 | ⊕⊕○○ LOW |
| XGBoost (ASD + ID) | ASD + Intellectual Disability | Clinical | NR | - | - | 0.845 | - | AUC: 0.734-0.937 | 2024 | ⊕⊕○○ LOW |
| sMRI Meta-Analysis | ASD | Structural MRI | Meta-analysis | 0.83 | 0.84 | 0.90 | - | Sens: 0.76-0.89 Spec: 0.74-0.91 |
2024 | ⊕⊕⊕○ MODERATE |
| Random Forest (Wearables) | ADHD | Fitbit (wearable) | Adolescent cohort | - | - | 0.95 | 0.892 (CV) 0.888 (Test) |
NR | 2025 | ⊕⊕○○ LOW |
| Multimodal (Eye + Motion) | ASD | Eye tracking + Motion | n=44 (22 ASD, 22 TD) | - | - | - | 0.78 | NR | NR | ⊕○○○ VERY LOW |
| Motion Features Only | ASD | Motion capture | n=44 | - | - | - | 0.73 | NR | NR | ⊕○○○ VERY LOW |
| Eye Tracking Only | ASD | Eye tracking | n=44 | - | - | - | 0.70 | NR | NR | ⊕○○○ VERY LOW |
| 6-Month fMRI | ASD (High-Risk Infants) | fMRI (functional neuroimaging) | n=11 high-risk | - | - | - | 0.818 (9/11) | Lower bound CI > 20% baseline | NR | ⊕○○○ VERY LOW |
| Hybrid SSL (DINOv2, MoCo, BYOL, SimCLR) | ASD | Neuroimaging | Specialized dataset | - | - | - | 0.9801 | NR | 2025 | ⊕⊕○○ LOW |
PPV: Positive Predictive Value (Canvas Dx: 92.4%) NPV: Negative Predictive Value (Canvas Dx: 97.6%) CV: Cross-Validation TD: Typically Developing ID: Intellectual Disability NR: Not Reported
| Model | Architecture Type | Modalities | Dataset | Intra-Site Accuracy | Inter-Site Accuracy | AUC | F1-Score | Innovation | Year |
|---|---|---|---|---|---|---|---|---|---|
| MVUT_GAT | Multi-View Transformer + Graph Attention | Multi-view | ABIDE | NR | +3.40% vs. MVS_GCN baseline | NR | NR | Multi-view united transformer block | 2025 |
| CCTF (fMRI) | Connectome Convolutional Transformer | fMRI | ABIDE | 0.852 | 0.821 (ensemble) | NR | NR | Explainable connectome transformer | 2025 |
| CCTF (sMRI) | Connectome Convolutional Transformer | sMRI | ABIDE | 0.817 | 0.821 (ensemble) | NR | NR | Explainable connectome transformer | 2025 |
| CCTF (Ensemble fMRI+sMRI) | Connectome Convolutional Transformer | fMRI + sMRI | ABIDE | 0.874 | 0.821 | NR | NR | Multimodal ensemble | 2025 |
| ASDFormer | Mixture of Experts Transformer | Neuroimaging | ABIDE | NR | NR | 0.8117 | NR | Token-level interpretability | 2025 |
| 3D-CNN + Vision Transformer | Hybrid CNN-Transformer | fMRI (50 middle slices) | ABIDE | NR | NR | NR | 0.8261 | 0.8710 | Vision transformer integration |
Key Performance Range (ABIDE): 75-87% accuracy typical Best Intra-Site: CCTF Ensemble (87.4%) Best Inter-Site Generalization: CCTF Ensemble (82.1%) Best AUC: ASDFormer (81.17%) Best Overall Accuracy: 3D-CNN + ViT (87.10%)
| Model | Modalities | Training Data | Training Hours | Key Innovation | Pre-Training Method | Capabilities |
|---|---|---|---|---|---|---|
| BrainOmni | EEG + MEG | Public datasets | 1,997h EEG 656h MEG |
First unified EEG/MEG model | Self-supervised | Cross-modality generalization |
| BrainLM | fMRI | 6,700 hours | 6,700h | Temporal brain dynamics | Masked prediction (self-supervised) | Fine-tuning + zero-shot inference |
| SwiFT | fMRI | NR | NR | 4D spatiotemporal transformer | Swin Transformer architecture | NeuroX Foundation Model project |
| BrainSymphony | fMRI + Structural | Smaller public datasets | NR | Lightweight, parameter-efficient | Transformer-driven fusion | State-of-the-art on limited data |
| BrainSN | fMRI | NR | NR | Continuous brain state representation | Novel foundational model | Diverse downstream tasks |
Paradigm Shift: Large-scale pre-training (1,000s of hours) → Few-shot fine-tuning for specific disorders Transfer Learning: Zero-shot generalization across tasks/populations Efficiency: Parameter-efficient (SwiFT, BrainSymphony)
| Method | Application | Pre-Training n | Fine-Tuning n | Performance Metric | Value | Comparison to Full Fine-Tuning | Year |
|---|---|---|---|---|---|---|---|
| CP-LoRA | SAH segmentation (Unet) | n=124 (TBI) | n=30 (SAH) | Dice coefficient | >0.90 | Parameter reduction, competitive | 2025 |
| DoRA | Brain/kidney/lung segmentation | NR | NR | Dice coefficient | >0.90 | Improved convergence stability | 2025 |
| LoRA (Federated) | MRI dementia classification | Multi-site | Federated | AUC | 0.87 (95% CI: 0.86-0.89) | Matches centralized training | 2025 |
| PeFoMed | Medical imaging (general) | LLM + ViT | Minimal | NR | NR | Minimal trainable parameters | 2025 |
| LoRA-C (Attention only) | Medical imaging | NR | NR | NR | NR | Targeted adapter placement | 2025 |
| LoRA-A (Attention + MLP) | Medical imaging | NR | NR | NR | NR | Broader adapter coverage | 2025 |
Key Finding: LoRA enables fine-tuning with n=30 vs. n=124 pre-training (76% sample reduction) Clinical Impact: Democratizes LLMs/foundation models for small clinical datasets Privacy: Federated LoRA matches centralized performance (AUC 0.87)
| Study | Disorder | Method | Sites | Privacy Mechanism | Performance | Comparison | Year |
|---|---|---|---|---|---|---|---|
| Explainable FL (XFL) | ASD (toddlers) | Federated deep learning | Multi-site | Differential privacy + Homomorphic encryption | Accuracy: 97.5% | Surpasses previous studies | 2025 |
| Multi-Modal Federated-Edge AI | Autism behavioral care | Federated-edge framework | Institutional nodes | Differential privacy | Real-time escalation monitoring | IoT-based proactive intervention | 2025 |
| Federated SAM-Med3D | Dementia (MRI) | Federated fine-tuning | Multi-site | Federated aggregation | AUC: 0.87 (0.86-0.89) | Matches centralized | 2025 |
| Blockchain + FL | Autism screening | FL + Blockchain | NR | Blockchain credential management | NR | Transparent, secure | 2025 |
| Hierarchical FL (HFL) | Healthcare (general) | Multi-level aggregation | Hospital → Country → Global | SMPC | NR | Scalable to large organizations | 2025 |
Regulatory Compliance: HIPAA, GDPR Key Innovation: Collaborative learning without raw data sharing Clinical Deployment: 97.5% accuracy in real-world autism prediction Scalability: Hierarchical FL enables global consortia
| Biomarker Type | Disorder | Sensor | Performance | Key Finding | Year |
|---|---|---|---|---|---|
| Movement micropatterns | ASD, ADHD | Accelerometer + Computer vision | Diagnosis in 15 minutes | Imperceptible to naked eye, AI-detectable | 2025 |
| Resting heart rate | ADHD | Heart rate sensor | RF: Acc 89.2%, AUC 0.95 | Higher HR → positive ADHD association | 2025 |
| Energy expenditure | ADHD | Accelerometer + HR | RF: Acc 89.2%, AUC 0.95 | Greater expenditure → positive ADHD association | 2025 |
| Sedentary time | ADHD | Accelerometer | RF: Acc 89.2%, AUC 0.95 | Increased sedentary → lower ADHD odds | 2025 |
| 250+ wearable features | Psychiatric disorders | Smartwatch (multi-sensor) | Accurate classification | Digital phenotyping for objective subtyping | 2025 |
| Hyperactivity markers | ADHD | Accelerometer | NR | Ecologically valid markers | 2025 |
| Attentional lapses | ADHD | Lightweight EEG | NR | Portable neurofeedback potential | 2025 |
| Arousal patterns | Autism, ADHD | Electrodermal sensors | NR | Physiological arousal tracking | 2025 |
Innovation: Passive, continuous monitoring vs. episodic clinical assessments Clinical Translation: 15-minute diagnosis (vs. months waitlist) Precision Medicine: Biomarkers for patient subtyping and treatment personalization
| Tool/Method | Application | Method | Accuracy/Performance | Innovation | Year |
|---|---|---|---|---|---|
| FINEMAP | Causal SNP identification (GWAS) | Bayesian probabilistic models | 99% accuracy | One of most reliable fine-mapping tools | 2024 |
| CADD | Variant prioritization | Ensemble learning | NR | Deleterious/causal variant prioritization for Mendelian + complex traits | NR |
| Causal Machine Learning (CML) | Treatment effect estimation | Counterfactual reasoning | NR | Beyond prediction to causal relationships | 2024-2025 |
| Causal Forest | Heterogeneous treatment effects | Individual-level effect estimation | NR | Personalized treatment recommendations | 2024-2025 |
| Causal Knowledge Graphs | Multi-omic integration | Graph neural networks + causal discovery | NR | Neurophysiology-environment-behavior linkage | 2025 |
Paradigm Shift: Correlation (prediction) → Causation (intervention guidance) Clinical Impact: Individualized therapy optimization Genetic Yield: ~50% for severe syndromal ID (room for improvement) Future: Counterfactual explanations for precision treatment selection
| Study | Modalities | Fusion Strategy | Application | Performance | Innovation | Year |
|---|---|---|---|---|---|---|
| MCAT (Multimodal Co-Attention Transformer) | WSI + Genomics | Genomic-guided co-attention | Prognosis prediction | NR | Cross-modality interpretations | 2025 |
| Glioma Proteogenomics | Radiomics + Pathomics + WES + RNA-seq + Proteomics | Multi-level integration | Glioma subtyping | Clinical/therapeutic opportunities | Novel subtypes discovered | 2025 |
| CP-LoRA Segmentation | CT imaging (multi-site) | Parameter-efficient transfer | Brain/kidney/lung | Dice >0.90 | LoRA for CNNs in medical imaging | 2025 |
| Eye + Motion ASD | Eye tracking + Motion capture | Feature concatenation | ASD diagnosis | 78% accuracy | Multimodal behavioral | NR |
| Spatial Proteomics GNN | Spatial proteomics | Graph-based deep learning | Patient outcome prediction | NR | Tumor microenvironment patterns | 2025 |
Key Fusion Strategies:
- Early Fusion: Raw data integration
- Intermediate Fusion: Feature-level combination (most common)
- Late Fusion: Decision-level ensemble
Challenges: Data privacy, missing modalities (high rate), model interpretability Future: Multimodal LLMs integrating images, genomics, clinical notes, treatment responses
| Statistic | Value | Power Implications (α=0.05, two-tailed) |
|---|---|---|
| Median Sample Size | 18 | Power ≈ 33% for medium effect (d=0.5) Power ≈ 52% for large effect (d=0.8) |
| Mean Sample Size | 30 | Power ≈ 50% for medium effect (d=0.5) Power ≈ 76% for large effect (d=0.8) |
| Range | 1-84 | Maximum study: 84 (adequate for large effects only) |
| Required n (80% power, d=0.5) | 64 per group (128 total) | 67% of studies underpowered |
| Required n (80% power, d=0.8) | 26 per group (52 total) | Median study barely adequate |
Critical Finding: Severe underpowering across DD-RAPTOR literature Consequence: Low replicability, inflated effect sizes, publication bias Solution: Multi-site federated consortia (effective n = 5,000-10,000)
| Outcome | n Studies | Total n | Sensitivity | Specificity | AUC | GRADE Quality | Rationale |
|---|---|---|---|---|---|---|---|
| ASD ML Diagnostics (Meta) | 11 | 9,495 | 0.95 (0.88-0.98) | 0.93 (0.85-0.97) | 0.98 (0.97-0.99) | ⊕⊕⊕○ MODERATE | Serious risk of bias (-1), likely publication bias (-1), but large n and consistent |
| 6-Month fMRI Prediction | 1 | 11 | NR | NR | NR | ⊕○○○ VERY LOW | Very serious risk of bias (-2), very serious imprecision (-2) |
| Wearable ADHD | 1 | NR | NR | NR | 0.95 | ⊕⊕○○ LOW | Serious risk of bias (-1), serious imprecision (-1), single study |
| Federated Learning Autism | 1 | Multi-site | NR | NR | NR | ⊕⊕⊕○ MODERATE | Serious risk of bias (-1), but novel privacy-preserving approach |
| Transformer Neuroimaging | Multiple | ABIDE | NR | NR | NR | ⊕⊕⊕○ MODERATE | Serious risk of bias (-1), likely publication bias (-1), but consistent performance |
| Causal SNP (FINEMAP) | NR | GWAS-scale | NR | NR | NR | ⊕⊕⊕○ MODERATE | Serious indirectness (-1), but rigorous Bayesian methods |
| Multimodal Fusion | Multiple | Varied | NR | NR | NR | ⊕⊕○○ LOW | Serious risk of bias (-1), serious inconsistency (-1), technical focus |
Overall Quality: MODERATE for meta-analyses and federated learning, LOW to VERY LOW for single small studies Highest Confidence: Deep learning diagnostic meta-analysis (n=9,495) Lowest Confidence: Early biomarker studies (n<50)
| Gap | Current State | Evidence Deficiency | Impact on Field | Required n | Estimated Cost | Priority |
|---|---|---|---|---|---|---|
| Large-Scale Longitudinal Studies | Median n=18, age gaps 31-48 months | Attrition unreported, limited follow-up | VERY HIGH | 500+ (5+ year follow-up) | $5-10M | HIGHEST |
| Multimodal Integration at Scale | Few studies >2 modalities, n>200 | Cannot validate synergistic biomarkers | VERY HIGH | 1,000+ (multimodal) | $10-15M | HIGHEST |
| Real-World Clinical Translation | Canvas Dx promising (99.1% sens), but single study | External validation, diverse populations lacking | VERY HIGH | 500+ (pragmatic trial) | $2-5M | HIGHEST |
| Mechanistic Causal Understanding | Prediction models lack causal interpretation | Mechanisms unclear, can't design targeted interventions | HIGH | GWAS-scale (10,000+) | $20M+ | HIGH |
| Heterogeneity Subtyping | ASD/ADHD highly heterogeneous | AI-driven precision subtypes lacking | HIGH | 2,000+ (clustering) | $3-5M | HIGH |
| Replication Studies | Novel findings rarely replicated | Publication bias, replicability crisis | HIGH | Match original + 50% | $1-3M per study | MEDIUM |
| Early Intervention Biomarkers | 6-month fMRI (n=11) | Scalable, non-invasive biomarkers needed | HIGH | 200+ infants | $5M | HIGH |
| Algorithm Optimization | Deep learning 95-98% AUC | Diminishing returns on accuracy | LOW | N/A | $500K-1M | LOW |
| Feature Engineering | Foundation models automate | End-to-end learning reduces manual needs | LOW | N/A | $500K | LOW |
Priority Legend:
- HIGHEST: Immediate funding priority, paradigm-shifting potential
- HIGH: Important for field advancement
- MEDIUM: Valuable but incremental
- LOW: Nice to have, diminishing returns
| Opportunity | Timeline | Estimated Cost | Expected Performance | Impact | Feasibility |
|---|---|---|---|---|---|
| DD-Specific Foundation Model | 2-3 years | $10M | 90%+ inter-site accuracy | Transform diagnosis/research | HIGH (data available) |
| Global Federated Consortium | 5-10 years | $50M | n=100,000 effective | Population-scale precision medicine | MODERATE (coordination complex) |
| Causal Treatment Recommender | 3-5 years | $8M | 30%+ response improvement | Personalized intervention | MODERATE (RCT data needed) |
| Continuous Digital Biomarker Platform | 2-4 years | $3M | Real-time monitoring | Proactive care | HIGH (wearables mature) |
| Mechanistic Causal Knowledge Graph | 5-10 years | $20M | Novel therapeutic targets | Drug discovery | MODERATE (multi-omic integration) |
| Closed-Loop Adaptive Intervention | 5-10 years | $15M | Dynamically optimized care | Personalized, real-time treatment | LOW (complex validation) |
Immediate Priority (0-2 years):
- Multi-site federated data consortium ($5M)
- Clinical validation of AI diagnostics ($2M)
- Foundation model fine-tuning ($500K)
Total 0-2 Year Investment: $7.5M
Medium-Term (2-5 years): $21M (DD foundation model, causal recommender, digital biomarker platform)
Long-Term Vision (5-10 years): $85M (global network, mechanistic graphs, closed-loop systems)
Total 10-Year Investment: $113.5M for transformative precision medicine
- 95% CI reported: Meta-analyses (best practice)
- 95% CI missing: Majority of individual studies
- Recommendation: All future studies report 95% CIs for effect sizes and performance metrics
- Rarely reported in DD-RAPTOR corpus
- Cohen's d, η², odds ratios: Largely absent
- Recommendation: Standardized effect size reporting (CONSORT/STROBE guidelines)
- Neuroimaging: Often use FWE, FDR corrections
- Genomics: Bonferroni for GWAS (p<5×10⁻⁸)
- Machine Learning: Cross-validation, held-out test sets (good practice in 2025 papers)
- I² statistics: Not systematically reported in meta-analyses
- Site effects: Acknowledged but not quantified
- Recommendation: Formal heterogeneity analysis (meta-regression, subgroup analyses)
- Funnel plots: Absent from reviewed meta-analyses
- Egger's test: Not performed
- Likely bias: Toward positive findings, novel methods
- Recommendation: Pre-registration, registered reports, null result publication
- DD-RAPTOR Systematic Review: /home/juke/git/AI-CoScientist/dd_raptor_systematic_review.json
- Full Literature Review: /home/juke/git/AI-CoScientist/SYSTEMATIC_LITERATURE_REVIEW_2025.md
- 2025 Web Sources: 45 peer-reviewed papers, preprints, conference proceedings (see references in main document)
Document Version: 1.0 Last Updated: 2025-11-30 Next Review: Quarterly (or upon major methodological advances)