Artificial intelligence platform for genetic counseling, hereditary cancer risk assessment, and family pedigree analysis

Abstract

The present invention relates to an artificial intelligence–driven platform designed for comprehensive genetic counseling, hereditary cancer risk assessment, and automated pedigree analysis. The invention integrates genomic data, clinical features, and multigenerational family history through advanced data modeling and machine learning algorithms to enable precise, scalable, and evidence-based genetic consultation. Traditional genetic counseling processes are labor-intensive, requiring manual pedigree construction and subjective evaluation of variant pathogenicity and familial inheritance. These limitations result in delayed risk stratification and inconsistent preventive recommendations. The disclosed platform overcomes these shortcomings by introducing an intelligent, interoperable system that automates the acquisition, normalization, and interpretation of heterogeneous genetic and clinical data to deliver real-time, individualized counseling outcomes. The system comprises five primary modules: (1) a data acquisition module, (2) a data processing and inference engine, (3) a pedigree generation and visualization unit, (4) a counseling and recommendation module, and (5) a secure cloud-based user interface. The data acquisition module receives and harmonizes multi-source inputs including genomic sequencing results, variant annotation files (such as VCF or BAM formats), patient demographics, electronic health records (EHRs), and family medical history collected via structured questionnaires or natural language processing (NLP) of unstructured clinical notes. The processing engine employs supervised and unsupervised machine-learning models trained on large-scale, anonymized datasets of hereditary cancer syndromes, enabling the prediction of gene-level pathogenicity, inheritance mode, and personalized disease susceptibility. The AI model calculates individualized hereditary risk scores, integrating polygenic risk factors and family aggregation metrics. The pedigree generation module automatically constructs interactive multigenerational family trees, highlighting affected members, carriers, and probable inheritance patterns using graph-based inference algorithms. This visualization facilitates intuitive understanding of familial transmission, carrier probability, and consanguinity. The counseling module translates computed risk profiles into actionable, evidence-based recommendations, aligned with international clinical guidelines such as NCCN, ACMG, and ESMO. These outputs include suggestions for confirmatory genetic tests, early screening programs, lifestyle modifications, and targeted preventive interventions. Reports are automatically generated for both clinicians and patients, promoting consistent and reproducible genetic counseling. The user interface operates as a secure, GDPR- and HIPAA- compliant digital platform accessible via web or API integration with hospital information systems. In certain embodiments, the platform supports federated learning across multiple institutions or countries, ensuring model improvement without direct data sharing, thereby preserving patient privacy and data sovereignty. In one embodiment, the system is validated across data cohorts from at least eight countries to ensure cross-population generalizability of its predictive models. The platform's modular design allows integration with additional AI-based oncology tools, digital pathology systems, and precision medicine workflows. Overall, the invention provides a robust and scalable technological infrastructure that transforms genetic counseling from a manual, expert-dependent process into a data-driven, automated, and globally interoperable service. By combining AI-based analytics, dynamic pedigree visualization, and standardized preventive guidance, the invention significantly enhances the accuracy, accessibility, and cost-effectiveness of hereditary cancer risk management.

Description

[0001] Artificial Intelligence Platform for Genetic Counseling, Hereditary Cancer Risk Assessment, and Family Pedigree Analysis

[0002] TECHNICAL FIELD

[0003] The present invention relates to artificial intelligence systems, biomedical informatics, and predictive healthcare analytics. More specifically, it concerns a web-based Al platform for hereditary cancer risk assessment through the integration of family history, genetic variant data, and demographic or phenotypic metadata. The system applies advanced deep learning models to predict individual cancer susceptibility and recommend appropriate genetic testing.

[0004] BACKGROUND

[0005] Cancer is one of the leading causes of mortality worldwide, and approximately 5-10% of all cancers are hereditary in origin. Hereditary cancer syndromes, such as Hereditary Breast and Ovarian Cancer (HBOC), Lynch Syndrome, and Familial Adenomatous Polyposis (FAP), arise from germline mutations passed down through family generations. Detecting hereditary cancer predisposition requires a comprehensive assessment of both genetic data and family history structures (pedigrees).

[0006] Existing clinical tools such as "Progeny", "Myriad RiskScore", or "BOADICEA" use rule -based or statistical models that depend on fixed parameters and limited datasets. These tools lack the adaptability, multi-modal learning capacity, and self-improving capabilities that Al-based systems can offer. Furthermore, they typically do not integrate diverse data sources such as clinical phenotypes, genomic variants, and family topology graphs within a unified computational framework.

[0007] Hereditary cancers such as breast, colorectal, ovarian, and prostate cancers often arise due to inherited pathogenic variants. Current genetic counseling methods rely heavily on manual pedigree analysis and subjective interpretation of variant data, which limits scalability and precision. The present invention provides a platform combining genomic data, family history, and Al-powered risk modeling, enabling rapid and standardized risk assessment and personalized clinical guidance. The invention overcomes these limitations through a web-based Al platform that automatically processes, encodes, and interprets complex hereditary relationships and genetic variant patterns to compute personalized cancer risk scores. The system is capable of recommending further genetic testing and surveillance strategies based on the model’s prediction confidence.

[0008] SUMMARY

[0009] The invention pertains to a next-generation artificial intelligence platform that facilitates comprehensive genetic counseling, predictive assessment of hereditary cancer risk, and automated analysis of family pedigrees. This system integrates genomic information, clinical indicators, and multigenerational familial health data through advanced computational modeling and machinelearning frameworks, enabling a highly accurate, scalable, and evidence-driven approach to genetic evaluation.

[0010] Conventional genetic counseling practices rely heavily on manual pedigree charting and subjective interpretation of genetic variants, which are resource-intensive and prone to inconsistencies. These traditional methods often lead to delays in identifying high-risk individuals and inconsistencies in preventive guidance. The disclosed invention addresses these challenges by introducing an intelligent, interoperable digital ecosystem that automates the collection, harmonization, and interpretation of diverse genetic and clinical datasets to produce individualized, real-time counseling insights.

[0011] The platform architecture consists of five core components:

[0012] (1) a data ingestion module,

[0013] (2) an Al-based analytical engine,

[0014] (3) a pedigree construction and visualization component,

[0015] (4) a recommendation and counseling subsystem, and

[0016] (5) a secure, cloud-enabled interface layer.

[0017] The data ingestion module gathers information from multiple sources, including whole-genome or targeted sequencing outputs, variant annotation data (such as VCF or BAM files), demographic details, electronic health records (EHRs), and structured or unstructured family history inputs. Unstructured data may be processed using natural language processing (NLP) techniques to extract relevant hereditary information.

[0018] The Al analytical engine utilizes supervised, unsupervised, and deep-learning algorithms trained on large, anonymized datasets representing hereditary cancer syndromes. The engine predicts variant pathogenicity, inheritance mechanisms, and patient-specific susceptibility to cancer by computing personalized hereditary risk indices that integrate polygenic factors and family aggregation parameters.

[0019] The pedigree module automatically generates dynamic, interactive family trees using graph-based inference models that highlight affected relatives, carriers, and probable inheritance paths. This visual representation simplifies the understanding of genetic transmission patterns, consanguinity relationships, and carrier probabilities for clinicians and patients alike.

[0020] The recommendation subsystem transforms computed risk results into actionable clinical insights based on established international standards, such as NCCN, ACMG, and ESMO guidelines, producing recommendations for confirmatory testing, early screening, behavioral modification, and tailored preventive interventions. The system automatically compiles structured counseling reports for both healthcare providers and patients to ensure uniformity and reproducibility of genetic assessments.

[0021] The interface layer is designed as a secure, compliant digital environment that meets GDPR and HIPAA standards. It supports seamless integration via APIs with existing healthcare and laboratory information systems. In certain embodiments, the platform enables federated learning collaboration across institutions or countries, allowing Al model enhancement without exchanging raw patient data, thus maintaining confidentiality and data ownership. In exemplary use cases, the system has been validated across population datasets from multiple regions, including at least eight distinct countries, ensuring global applicability and generalizability of its predictive models. The platform’s modular architecture also allows expansion to integrate with other Al-driven oncology systems, digital pathology solutions, or precision-medicine pipelines.

[0022] In essence, the invention establishes a powerful and extensible technological framework that redefines genetic counseling, shifting it from an expert-dependent manual process to a data-centric, automated, and globally connected service. By combining intelligent analytics, adaptive pedigree visualization, and standardized prevention workflows, the invention markedly advances the precision, accessibility, and efficiency of hereditary cancer risk management. DETAILED DESCRIPTION OF THE INVENTION

[0023] The system is divided into multiple modules:

[0024] 1. Data Acquisition Layer-Responsible for collecting and harmonizing user data including pedigree structures, phenotypic metadata, and genetic test files (VCF, FASTA, or gene panels).

[0025] 2. Preprocessing Engine-Performs data cleaning, imputation of missing values, variant normalization, and relationship graph construction.

[0026] 3. Al Risk Modeling Module-Uses hybrid neural architectures (CNN + Transformer + Graph Encoder) to compute risk probabilities.

[0027] 4. Recommendation Engine-Suggests relevant genetic panels or tests according to the predicted hereditary risk.

[0028] 5. Web Interface and API Lay er- Allows clinicians and users to upload data, visualize risk outputs, and integrate the system with EMR or lab databases.

[0029] Data Representation

[0030] The data are represented in three major domains:

[0031] 1. Pedigree Graph Representation

[0032] Each individual in a family is represented as a node, with edges defining parental relationships. Graph-based embeddings allow the neural network to capture inheritance patterns and shared genetic backgrounds.

[0033] 2. Genetic Variant Encoding

[0034] Genetic variants (e.g., BRCA1 c.5266dupC) are represented as sequences of allelic features, which are processed by a Transformer encoder.

[0035] Features include gene name, variant type (missense, nonsense, etc.), position, zygosity, and clinical significance from databases such as ClinVar and gnomAD.

[0036] 3. Phenotype and Clinical Metadata

[0037] Age, sex, tumor history, and lifestyle features are incorporated into the model via dense embedding layers.

[0038] Algorithmic Design

[0039] The core risk prediction model integrates the following layers:

[0040] 1. Graph Neural Network (GNN): For encoding family relationships and hereditary transmission probabilities.

[0041] 2. Transformer Encoder: For processing high-dimensional variant embeddings. 3. CNN-based Feature Extractor: For detecting complex hierarchical feature interactions between variant and phenotype representations.

[0042] 4. Fully Connected Classifier: For producing the final hereditary risk score (low, moderate, or high).

[0043] Py Torch Implementation

[0044] Below is a simplified implementation outline of the model architecture in Py Torch. import torch import torch, nn as nn import torch, nn. functional as F class FamilyGraphEncoder(nn.Module): def init (self, input_dim=64, hidden_dim=128): superQ. init () selffcl = nn.Linear(input_dim, hidden dim) self.fc2 = nn.Linear(hidden_dim, hidden dim) def forward(self, x, adj): h = torch. relu(self.fcl (torch. matmul (adj, x))) h = self. fc2(torch. matmul (adj, h)) return h.mean(dim=l) class VariantTransformer(nn.Module): def init (self, input_dim=512, num_heads=8, num_layers=4): superQ. init () encoder layer = nn.TransformerEncoderLayer(d_model=input_dim, nhead=num_heads) self. transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers) self. linear = nn.Linear(input_dim, 256) def forward(self, x): out = self.transformer(x) return self. linear(out. mean(dim= 1 )) class RiskPredictor(nn. Module): def init (self, graph_dim=128, gene_dim=256, meta_dim=64, num_classes=3): superQ. init () selffc = nn. Sequential nn.Linear(graph_dim + gene_dim + meta_dim, 256), nn.ReLUQ, nn.Linear(256, num_classes), nn. S oftmax(dim= 1 )

[0045] ) def forward(self, g_feat, v_feat, meta): combined = torch. cat([g_feat, v feat, meta], dim=l) return self.fc(combined) class HereditaryCancerRiskModel(nn.Module): def init (self): superQ. init () selfgraph enc = Family GraphEncoderQ selfvar enc = VariantTransformerQ self, predictor = RiskPredictorQ def forward(self, family _x, adj, variant_x, meta): g_feat = self.graph_enc(family_x, adj) v_feat = self.var_enc(variant_x) out = self. predictor(g_f eat, v feat, meta) return out

[0046] Training and Optimization

[0047] The model is trained on anonymized datasets consisting of known hereditary cancer syndromes labeled by expert geneticists. Loss is computed using cross-entropy, and optimization is performed via Adam optimizer with cyclical learning rate scheduling. model = HereditaryCancerRiskModelQ optimizer = torch. optim.Adam(model.parameters(), lr=le-4) criterion = nn.CrossEntropyLossQ for epoch in range(lOO): optimizer. zero gradQ outputs = model(family_x, adj matrix, variant x, metadata) loss = criterion(outputs, labels) loss.backward() optimizer. stepQ

[0048] Web Platform Integration

[0049] The trained model is deployed as a RESTful API service accessible via HTTPS. The backend (e.g., Flask or FastAPI) manages data uploads, model inference, and secure storage using encrypted databases. The frontend visualizes pedigree graphs and risk scores interactively using JavaScript libraries such as D3.js.

[0050] The API structure includes:

[0051] - ' / upload' : accepts JSON or CSV files of family data and genetic variants

[0052] - ' / predict' : returns hereditary cancer risk classification and recommended tests

[0053] - ' / explain' : provides feature attribution via SHAP or Grad-CAM for transparency

[0054] Risk Interpretation and Genetic Test Recommendation

[0055] The model output includes:

[0056] - Hereditary risk score (probability between 0 and 1)

[0057] - Risk classification (Low, Intermediate, High) - Gene panel recommendations (e.g., BRCA, MMR, TP53, PTEN panels)

[0058] - Optional suggestions for cascade testing of family members

[0059] The recommendation engine is implemented using a ruleset derived from NCCN and ACMG guidelines, modified by the Al model’s confidence levels.

Claims

ClaimsWhat is claimed is:

1. A comprehensive artificial intelligence platform for multi-modal radiological image analysis comprising: o a preprocessing module configured for image normalization; o a neural network-based localization module configured to detect the heart and generate a bounding box; o a preprocessing module for image normalization and modality-specific feature extraction; o a tumor detection module employing a hybrid CNN-Transf ormer architecture; o a 3D volumetric segmentation module with attention-based skip connections; o a tumor grading module predicting malignancy probability scores; o a quantitative volumetric changes for evaluating therapy response; o a segmentation module configured to delineate cardiac, pulmonary, and oncological structures; and o an integration module configured to interface with DICOM and PACS systems.

2. The platform of claim 1, wherein the tumor grading module integrates radiomic, morphological, and PET-derived metabolic features via CNN, Vision Transformer (ViT), Radiomics + ML (SVM, XGBoost), Hybrid CNN-ML models to identify tumour masses, segmentation, volumetric quantification, and grading to guide surgical or chemotherapeutic interventions.

3. The platform of claim 1, wherein the output includes attention maps, segmentation masks, tumor volume, shape metrics, and malignancy probability scores to increase diagnostic capacity, segmentation, tumor volume assessment, malignancy grading for staging and treatment planning.

4. The platform of claim 1, wherein a cardiac MRI analysis module via cNeural Networks (CNNs) and U-Net variants for segmentation (2D, 2.5D, or 3D) measures atrial and ventricular volumes and detects atrial fibrillation or mitral valve stenosis.

5. The platform of claim 1, wherein the cardiac localization module via nnU- Net / DeepLabV3+ / YOLOv8 and XGBoost / ResNet algorithm combinations calculates the cardiothoracic ratio and detects heart displacement.

6. The platform of claim 1, wherein the federated learning framework enables distributed training without sharing raw patient data.

7. The platform of claim 1, wherein the system is capable of analyzing CT, MRI, and PET / CT scans interchangeably to provide clinicians with reproducible, interpretable results, reducing diagnostic errors and variability8. The platform of claim 1, wherein a pneumonia classification module detects and classifies radiographic signs of pneumonia in chest X-rays.

9. The platform of claim 1, wherein a lung tumor segmentation module detects and quantifies tumor size and volume in CT images for staging analysis.

10. The platform of claim 1, wherein the system provides visualization overlays including bounding boxes, heatmaps, and segmentation contours on a DICOM viewer.

11. The platform of claim 1, wherein the system employs deep learning models selected from convolutional neural networks, transformers, or hybrid architectures.

12. The platform of claim 1, wherein model training is performed using federated learning for continuous adaptation while preserving patient privacy.