Integrated multimodal ai hospital platform with autonomous screening interval generation, digital-twin-driven therapy optimization, and closed-loop cancer management system

Abstract

The invention relates to an integrated multimodal artificial intelligence platform designed to function as an autonomous hospital system providing end-to-end health prevention, screening, diagnosis, treatment optimization, and longitudinal digital-twin-based monitoring. The platform introduces a closed-loop clinical architecture that continuously analyzes heterogeneous patient data including radiology, pathology, genomics, laboratory findings, longitudinal clinical records, wearable streams, and environmental exposures. A multimodal transformer (MT-X) generates a unified patient-specific representation, enabling high-precision diagnostic and prognostic inference. A novel Autonomous Screening Interval Generator (ASIG) dynamically determines individualized screening schedules based on calibrated risk models and temporal disease-evolution forecasting. A Digital Twin Engine (DTE) simulates tumor progression, metastasis probability, toxicity trajectories, and therapy response. An Adaptive Therapy Optimization Engine (ATOE), based on reinforcement learning, identifies optimal treatment strategies tailored to patient biology and system-level constraints. The invention is industrially applicable to hospitals, centers, national screening programs, tele-networks, and Al-enabled health systems. The integrated nature of the invention, the closed-loop framework, and the combination of digital-twin simulation with intelligent screening and therapy design constitute a substantial improvement beyond conventional medical Al solutions.

Description

[0001] Integrated Multimodal Al Hospital Platform with Autonomous Screening Interval Generation, Digital-Twin-Driven Therapy Optimization, and Closed-Loop Cancer Management System TECHNICAL FIELD

[0002] This invention relates to artificial intelligence systems for oncology, cardiovascular diseases, autonomous hospital management, precision diagnostics, personalized prevention care, and adaptive treatment optimization. The present invention relates to healthcare systems and medical informatics, specifically to an integrated Al platform that combines predictive Al analytics, preventive interventions, genetic counseling, intelligent screening, automated care pathways, and patient engagement for comprehensive healthcare delivery.

[0003] BACKGROUND

[0004] Cancer and cardiovascular diseases remain the leading causes of morbidity and mortality worldwide. Despite remarkable advancements in molecular diagnostics, immunotherapy, targeted therapy, and minimally invasive surgery, the global burden continues to rise, particularly due to delayed detection, inconsistent screening adherence, limited access to specialized oncologic care, and the complexity of clinical decision-making across heterogeneous patient populations. Traditional healthcare infrastructures face persistent challenges in providing timely, consistent, and high-quality care, especially in low-resource settings, geographically dispersed populations, and institutions experiencing shortages of specialists.

[0005] Conventional care systems typically operate in linear, fragmented workflows. Screening programs rely on fixed-interval protocols rather than personalized risk-adjusted schedules. Diagnostic evaluation often requires manual correlation between radiology, pathology, molecular profiling, and clinical data, resulting in delays and variability in interpretation. Treatment planning is similarly fragmented: oncologists must synthesize large volumes of multimodal data to determine optimal therapy regimens, yet existing electronic medical record (EMR) systems provide limited computational support, and conventional decision-support tools lack integration across modalities. Predicting treatment response or toxicity often depends on subjective clinical judgment, and there is no real-time simulation model that represents the dynamic biological evolution of tumors under different treatment strategies.

[0006] Artificial intelligence (Al) has emerged as a promising solution to several components of cancer care. Deep learning algorithms have demonstrated high accuracy in radiology interpretation, pathology slide analysis, and genomic variant classification. Machine-learning-based prognostic models have been introduced for specific cancers. However, these Al systems are typically narrow, task-specific, and siloed; they do not operate as cohesive, generalizable frameworks capable of managing the entire cancer care continuum. Most importantly, current Al approaches do not provide a unified, multimodal patient representation that integrates all available clinical, biological, and imaging data. They also lack the capability to autonomously plan screening schedules, simulate disease progression, optimize therapeutic strategies, or coordinate hospitallevel workflows.

[0007] Digital twin technologies, widely explored in industrial and engineering domains, have only recently entered biomedical research. Initial medical digital twins focus predominantly on cardiovascular physiology, orthopedic biomechanics, or critical care patient monitoring. While the conceptual promise of digital twins in oncology has been discussed, there is no clinically deployed system capable of generating dynamic, patient-specific cancer models that update continuously with new multimodal data and guide real-time therapy optimization. Existing cancer models are static, single-modality, and not integrated with clinical decision systems.

[0008] Furthermore, healthcare delivery systems lack closed-loop architectures. A closed-loop system requires: (1) continuous data intake, (2) computational interpretation, (3) automated action recommendations, (4) outcome measurement, and (5) recalibration. Current medical infrastructures do not support such iterative feedback cycles. Screening programs are static rather than adaptive; diagnosis is episodic rather than continuous; treatment planning is reactive rather than predictive; and follow-up monitoring is manual rather than model-driven.

[0009] In addition, hospitals face operational challenges in maintaining timely and coordinated oncology services. Radiology and pathology departments often operate independently. Genomic laboratories may deliver results in formats that clinicians struggle to integrate. Tumor boards are time-limited and resource-intensive, leading to delays in complex cases. Clinical guidelines (e.g., NCCN, ESMO, ASCO) are updated frequently, yet physicians must manually interpret and apply these updates in each case. As cancer datasets grow exponentially, clinicians increasingly rely on experience and heuristics rather than systematic data-driven guidance.

[0010] Thus, there is an urgent need for a holistic, integrated, Al-driven platform capable of supporting — and in some components autonomously executing — the entire cancer care spectrum from prevention and personalized screening through diagnosis, treatment selection, and longitudinal monitoring. Such a platform must integrate multimodal data, operate at hospital scale, and enable real-time decision-making. It must incorporate predictive modelling, digital-twin simulation, risk stratification, and adaptive treatment optimization. It must operate as a closed-loop ecosystem that continuously learns from patient outcomes and refines its predictions and recommendations. Despite rapid development in Al, no existing system delivers:

[0011] (1) unified multimodal oncology representation;

[0012] (2) personalized autonomous screening interval generation;

[0013] (3) real-time patient-specific digital twin modeling;

[0014] (4) reinforcement-learning-based adaptive therapy optimization;

[0015] (5) integration across hospital infrastructures including PACS, LIS, EMR, genetic labs, and remote sensors;

[0016] (6) a fully closed-loop management framework.

[0017] Accordingly, there remains a significant unmet need for an integrated platform capable of providing comprehensive precision oncology care across prevention, screening, diagnosis, therapy design, monitoring, and recurrence prediction. The background barriers include fragmented workflows, incompatible software systems, absence of multimodal fusion, limited automation, and a lack of predictive, simulation-driven clinical intelligence. Addressing this gap requires an invention that combines Al models, system-level orchestration, digital-twin technology, and clinical guidelines into a scalable, operational, and industrial hospital framework.

[0018] SUMMARY

[0019] This invention introduces a first-of-its-kind Al-driven autonomous ecosystem that operates across the entire cancer care continuum. Unlike existing standalone Al diagnostic models, this invention defines a fully integrated Al hospital architecture for oncology, cardiovascular diseases, and non- communicative diseases, consisting of the following novel components: 1. Multimodal Transformer (MT-X)

[0020] • A transformer architecture capable of true multimodal fusion of:

[0021] • Radiology (CT, MRI, PET, Mammography, Ultrasound)

[0022] • Pathology Whole Slide Images (WSI)

[0023] • Genomics (NGS, RNA-seq, liquid biopsy)

[0024] • Laboratory markers

[0025] • Electronic medical records

[0026] • Wearable and lifestyle data

[0027] The fusion occurs inside a single joint latent space, not a pipeline of separate models.

[0028] 2. Autonomous Screening Interval Generator (ASIG)

[0029] • An engine that calculates dynamic, real-time, personalized screening intervals for cancer (e.g., breast, lung, colorectal) using:

[0030] • Individual risk profiles

[0031] • Polygenic risk

[0032] • Environmental exposures

[0033] • Prior imaging

[0034] • Social determinants

[0035] • Al-predicted tumor evolution

[0036] 3. Digital Twin Engine (DTE)

[0037] The system builds a dynamic cancer digital twin that continuously models:

[0038] • Tumor progression

[0039] • Treatment response

[0040] • Metastasis probability

[0041] • Toxicity risks

[0042] • Survival probabilities

[0043] The digital twin updates with each new data entry.

[0044] 4. Adaptive Therapy Optimization Engine (ATOE)

[0045] • A reinforcement-learning treatment engine that:

[0046] • Simulates multiple therapy trajectories

[0047] • Evaluates toxicity, cost, accessibility

[0048] • Predicts expected response using the digital twin

[0049] • Recommends optimal therapy sequences

[0050] 5. Autonomous Clinical Pathway Generator (ACPG) — Novelty

[0051] • Generates step-by-step oncology management workflows:

[0052] • Screening — Diagnostic — Staging — Treatment

[0053] • Predictive follow-up plan

[0054] • Alerting and tumor board automation

[0055] 6. Hospital-Level Al Orchestration Layer (H-AIOL) — Industrial Step

[0056] • Integrates all subsystems across:

[0057] • Radiology PACS

[0058] • Pathology LIS

[0059] • Oncology EMR Operating rooms

[0060] Pharmacy

[0061] National registries

[0062] BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Fig.l: Illustrates architecture diagram and data-flow diagram of hospital-integration map by focusing closed-loop oncology cycle, MT-X + ASIG + DTOE + ATOE Layout DETAILED DESCRIPTION OF THE INVENTION

[0064] The proposed Al hospital platform is a comprehensive, integrated system that unites predictive analytics, automated care pathways, genetic counseling, intelligent imaging, laboratory analysis, and patient engagement. Unlike conventional Al healthcare tools, which are siloed and domainspecific, this platform leverages multi-modal data fusion to generate personalized, preventive, and adaptive care across multiple disease domains, including oncology, cardiovascular diseases, diabetes, and other non-communicable diseases (NCDs).

[0065] The system is structured into seven core modules:

[0066] • Data Ingestion and Preprocessing Module

[0067] • Predictive Analytics Engine

[0068] • Diagnostic Imaging Module

[0069] • Genetic Counseling Module

[0070] • Automated Care Pathways Module

[0071] • Patient Engagement Module

[0072] • Adaptive Learning & Feedback Loop

[0073] Each module operates independently yet communicates seamlessly with the others, creating a closed-loop ecosystem capable of continuous improvement.

[0074] 2. Data Ingestion and Preprocessing Module

[0075] 2.1 Multi-modal Data Collection

[0076] • The platform collects and stores multiple types of patient data:

[0077] • Structured Clinical Data: Demographics, vital signs, comorbidities, medication history • Laboratory Data: Hematology, biochemistry, microbiology, pathology results

[0078] • Genetic Data: SNP panels, whole-exome sequencing, polygenic risk scores

[0079] • Medical Imaging Data: Radiology (CT, MRI, X-ray), histopathology slides

[0080] All data is encrypted and stored in a centralized, HIPAA-compliant database, ensuring both security and accessibility for Al model training.

[0081] 2.2 Data Cleaning and Normalization

[0082] To maximize Al performance, preprocessing involves:

[0083] • Imputation of missing values using K-nearest neighbors or matrix completion methods • Scaling continuous variables (e.g., blood pressure, BMI) to zero mean and unit variance • Encoding categorical variables with one-hot encoding or embedding layers for neural networks

[0084] • Image augmentation: rotations, flips, contrast adjustment, Gaussian noise injection PyTorch Example: Clinical Data Normalization

[0085] import torch

[0086] import pandas as pd

[0087] import numpy as np

[0088] clinical_df = pd.read_csv("clinical_data.csv")

[0089] features = clinical_df[['age','bmi','blood_pressure']].values

[0090] features = (features - features.mean(axis=0)) / features.std(axis=0)

[0091] clinical_tensor = torch.tensor(features, dtype=torch.float32)

[0092] PyTorch Example: Image Preprocessing

[0093] from torchvision import transforms

[0094] image_transforms = transforms.Compose([

[0095] transforms.Resize((224,224)),

[0096] transforms.RandomHorizontalFlip(),

[0097] transforms.RandomRotation(15),

[0098] transforms.ToTensor(),

[0099] transforms.Normalize(mean=[0.485,0.456,0.406],

[0100] std=[0.229,0.224,0.225])

[0101] ])

[0102] 2.3 Feature Extraction and Encoding

[0103] • Clinical and lab features are passed through a dense embedding network

[0104] • Genetic features (SNPs, polygenic risk scores) are encoded using linear layers or autoencoders

[0105] • Image embeddings are generated using CNNs or Vision Transformers

[0106] Example: Genetic Embedding

[0107] genetic_data = torch.randn(clinical_tensor.shape[0], 1000) # Example SNPs

[0108] genetic_embedding_layer = torch.nn.Linear(1000, 64)

[0109] genetic_embedding = genetic_embedding_layer(genetic_data)

[0110] 3. Predictive Analytics Engine

[0111] 3.1 Architecture Overview

[0112] • The predictive engine combines multiple learning paradigms to ensure robust risk prediction:

[0113] • Deep Learning (DL): Handles unstructured data (images, time-series lab data)

[0114] • Gradient Boosting / Random Forests: Structured clinical and lab data

[0115] • Bayesian Inference Models: Probabilistic disease risk estimation

[0116] • Ensemble Fusion: Combines outputs from multiple models to generate final predictions 3.2 Multi-modal Feature Fusion

[0117] • The fusion mechanism combines embeddings from all data types:

[0118] • Concatenation of clinical, genetic, and imaging embeddings Attention-based weighting for feature relevance

[0119] Optional dimensionality reduction using autoencoders or PCA

[0120] Py Torch Example: Multi-modal Fusion Network

[0121] import torch.nn as nn

[0122] import torch.nn.functional as F

[0123] class MultiModalRiskModel(nn. Module):

[0124] def __init__(self, clinical_dim, genetic_dim, image_dim, hidden=256): super(MultiModalRiskModel, self).__init__()

[0125] self.fc1 = nn.Linear(clinical_dim + genetic_dim + image_dim, hidden)

[0126] self.fc2 = nn.Linear(hidden, hidden / / 2)

[0127] self.fc3 = nn.Linear(hidden / / 2, 1) # Output: risk score

[0128] def forward(self, clinical, genetic, image):

[0129] x = torch.cat([clinical, genetic, image], dim=1)

[0130] x = F.relu(self.fc1(x))

[0131] x = F.relu(self.fc2(x))

[0132] risk_score = torch.sigmoid(self.fc3(x))

[0133] return risk_score

[0134] clinical_dim = clinical_tensor.shape[1]

[0135] genetic_dim = 64

[0136] image_dim = 2048

[0137] model = MultiModalRiskModel(clinical_dim, genetic_dim, image_dim)

[0138] risk_scores = model(clinical_tensor, genetic_embedding, image_features)

[0139] 3.3 Disease Prediction and Interpretation

[0140] • Predicts disease-specific risk scores (0-1 probability)

[0141] • Provides interpretability via SHAP values or attention maps

[0142] • Generates recommendations for screening intervals, preventive interventions, and followups

[0143] 4. Diagnostic Imaging Module

[0144] 4.1 Radiology and Histopathology Analysis

[0145] • The imaging module applies state-of-the-art CNNs and Vision Transformers (ViT):

[0146] • ResNet / EfficientNet for feature extraction

[0147] • Vision Transformers for high-resolution whole-slide images

[0148] • Multi-task learning for segmentation, lesion detection, classification

[0149] PyTorch Example: CNN Feature Extraction import torchvision.models as models

[0150] resnet = models.resnet50(pretrained=True)

[0151] resnet.fc = nn.Identity()

[0152] image_batch = torch.randn(16,3,224,224)

[0153] image_embeddings = resnet(image_batch)

[0154] PyTorch Example: Vision Transformer Embedding

[0155] from torchvision.models import vit_b_16

[0156] vit_model = vit_b_16(pretrained=True)

[0157] vit_model.heads = nn.Identity()

[0158] vit_features = vit_model(image_batch)

[0159] 4.2 Integration with Multi-modal Data

[0160] Image embeddings are fused with clinical and genetic embeddings to produce personalized diagnostic insights.

[0161] 5. Genetic Counseling Module

[0162] 5.1 Polygenic Risk Score (PRS) Computation

[0163] • Computes PRS using linear combination of SNP effects

[0164] • Adjusts for family history and demographic factors

[0165] 5.2 Al-assisted Counseling

[0166] • Generates personalized recommendations based on PRS, clinical history, and lab data • Supports risk stratification and early intervention planning

[0167] PyTorch Example: Genetic + Clinical Embedding Fusion

[0168] genetic embedding = torch.nn. Linear(1000, 64)(genetic_data)

[0169] combined embedding = torch. cat([clinical_tensor, genetic embedding], dim=l)

[0170] 6. Automated Care Pathways Module

[0171] • Generates dynamic, patient-specific care plans

[0172] • Uses predictive risk scores, imaging findings, and genetic data

[0173] • Adapts pathways based on real-time outcomes and adherence

[0174] Adaptive Workflow Example (Pseudo-PyTorch)

[0175] for patient in patients:

[0176] risk = model(clinical, genetic, image)

[0177] if risk > high risk threshold:

[0178] schedule followup(patient)

[0179] notify clinician(patient)

[0180] update model(patient feedback) 7. Patient Engagement Module

[0181] • Interactive dashboard for accessing care plans

[0182] • Personalized reminders and alerts

[0183] • ecure communication channel with clinicians

[0184] • Data collected for adaptive learning

[0185] 8. Adaptive Learning & Feedback Loop

[0186] • Continuous retraining on new patient data

[0187] • Model performance monitored via metrics: AUC, sensitivity, specificity • Ensures predictive accuracy improves over time

[0188] 9. Workflow Summary

[0189] • Patient data ingestion — preprocessing — feature embedding

[0190] • Multi-modal fusion — predictive analytics — risk scoring

[0191] • Diagnostic imaging analysis — combined diagnostic insights

[0192] • Genetic counseling — personalized risk assessment

[0193] • Automated care pathway — dynamic follow-up and preventive interventions Patient engagzment → feedback → adaptive learning loop

[0194] class AtriumSegmentation(pl. LightningModule):

[0195] def __init__(self):

[0196] super(AtriumSegmentation,self).__init__()

[0197] self.training step outputs = []

[0198] self.validation_step_outputs = []

[0199] self.model = UNet()

[0200] self.optimizer = torch.optim.Adam(self.model.parameters(), lr = 1e-4)

[0201] self.loss_fn = DiceLoss()

[0202] def forward(self, data):

[0203] return torch.sigmoid(self.model(data))

[0204] def training_step(self, batch, batch_idx):

[0205] mri, mask = batch

[0206] mask = mask.float()

[0207] pred = self(mri)

[0208] loss = self.loss_fn(pred, mask)

[0209] self.training_step_outputs.append(loss)

[0210] self.log("Training Dice", loss)

[0211] if batch_idx % 50 ==0 :

[0212] self.log_images(mri.cpu() , pred.cpu() , mask.cpu() , "Train" ) return loss

[0213] def validation_step(self, batch, batch_idx):

[0214] mri, mask = batch

[0215] mask = mask.float()

[0216] pred = self(mri)

[0217] loss = self.loss_fn(pred, mask)

[0218] self.validation_step_outputs.append(loss)

[0219] self.log("Val Dice", loss)

[0220] if batch_idx % 2 ==0:

[0221] self.log_images(mri.cpu(), pred.cpu(), mask.cpu(), "Val" )

[0222] return loss

[0223] def log_images(self, mri, pred, mask, name):

[0224] pred = pred > 0.5

[0225] fig, axis = plt.subplots(1,2)

[0226] axis[0].imshow(mri[0][0], cmap="bone")

[0227] mask_ = np.ma.masked_where(mask[0][0]==0, mask[0][0])

[0228] axis[0].imshow(mask_, alpha = 0.6)

[0229] axis[1].imshow(mri[0][0], cmap="bone")

[0230] mask_ = np.ma.masked_where(pred[0][0]==0, pred[0][0])

[0231] axis[1].imshow(mask_, alpha = 0.6)

[0232] self.logger.experiment.add_figure(name, fig, self.global_step)

[0233] def configure_optimizers(self):

[0234] return [self.optimizer]

[0235] class CardiacDetectionModel(torch.nn. Module):

[0236] def __init__(self):

[0237] super().__init__()

[0238] self.model = torchvision.models.resnet18(pretrained=False)

[0239] self.model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)

[0240] self.model.fc = torch.nn. Linear(in_features=512, out_features=4) def forward(self, data):

[0241] return self.model(data)

[0242] def load_model(model_path):

[0243] """ Load the trained cardiac detection model"""

[0244] device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = CardiacDetectionModel()

[0245] model.load_state_dict(torch.load(model_path, map_location=device)) model.eval()

[0246] return model, device

[0247] def preprocess_dicom(dicom_path, target_size=224):

[0248] """ Preprocess DICOM image for model input"""

[0249] # Read DICOM file

[0250] dcm = pydicom.dcmread(dicom_path)

[0251] dcm_array = dcm.pixel_array

[0252] original_height, original_width = dcm_array.shape

[0253] # Resize and normalize for model input

[0254] dcm_array_resized = cv2.resize(dcm_array, (target_size, target_size)) dcm_array_resized = (dcm_array_resized / 255).astype(np.float32)

[0255] # Normalize with dataset statistics

[0256] dcm_array_resized = (dcm_array_resized - 0.494) / 0.252

[0257] # Convert to tensor

[0258] tensor = torch.tensor(dcm_array_resized).unsqueeze(0).unsqueeze(0) return tensor, dcm_array, original_height, original_width

[0259] def detect_cardiac(model, device, input_tensor):

[0260] """ Detect cardiac region using the model"""

[0261] with torch.no_grad():

[0262] input tensor = input_tensor.to(device)

[0263] prediction = model(input_tensor)

[0264] return prediction.cpu().numpy()[0]

[0265] def scale_bbox_coordinates(bbox_coords, original_size, target_size=224):

[0266] """ Scale bounding box coordinates from model output to original image size """ x0, y0, x1, y1 = bbox_coords

[0267] orig_h, orig_w = original_size # Calculate scaling factors

[0268] scale_x = orig_w / target_size

[0269] scale_y = orig_h / target_size

[0270] # Scale coordinates

[0271] x0_scaled = int(x0 * scale_x)

[0272] y0_scaled = int(y0 * scale_y)

[0273] x1_scaled = int(x1 * scale_x)

[0274] y1_scaled = int(y1 * scale_y)

[0275] return [x0_scaled, y0_scaled, x1_scaled, y1_scaled]

[0276] def resize_image_with_bbox(image_array, bbox_coords, output_size=500):

[0277] """ Resize image to desired output size while maintaining bounding box coordinates""" orig_h, orig_w = image_array. shape

[0278] # Calculate scaling factors

[0279] scale = min(output_size / orig_w, output_size / orig_h)

[0280] new_w = int(orig_w * scale)

[0281] new_h = int(orig_h * scale)

[0282] # Resize image

[0283] if isinstance(image_array, np.ndarray):

[0284] resized_image = cv2.resize(image_array, (new_w, new_h))

[0285] else:

[0286] resized_image = image_array.resize((new_w, new_h), Image. Resampling. LANCZOS) # Scale bounding box coordinates

[0287] x0, y0, x1, y1 = bbox_coords

[0288] x0_scaled = int(x0 * scale)

[0289] y0_scaled = int(y0 * scale)

[0290] x1_scaled = int(x1 * scale)

[0291] y1_scaled = int(y1 * scale)

[0292] return resized_image, [x0_scaled, y0_scaled, x1_scaled, y1_scaled]

[0293] def draw_bounding_box(image_array, bbox_coords):

[0294] """ Draw bounding box on the image"""

[0295] # Convert to PIL Image if it's a numpy array

[0296] if isinstance(image_array, np.ndarray):

[0297] if image_array.dtype!= np.uint8:

[0298] # Normalize to 0-255 range

[0299] image_array = ((image_array - image_array.min()) /

[0300] (image_array.max() - image_array.min()) * 255).astype(np.uint8) pil_image = Image. fromarray(image_array) else:

[0301] pil_image = image_array

[0302] # Draw bounding box

[0303] draw = ImageDraw. Draw(pil_image)

[0304] x0, y0, x1, y1 = bbox_coords

[0305] draw.rectangle([x0, y0, x1, y1], outline="orange", width=3)

[0306] return pil_image

[0307] class LungTumorSegmentation(pl. LightningModule):

[0308] def __init__(self):

[0309] super(LungTumorSegmentation,self).__init__() self.training_step_outputs = []

[0310] self.validation_step_outputs = []

[0311] self.model = UNet()

[0312] self.optimizer = torch.optim.Adam(self.model.parameters(), lr = 1e-4) self.loss_fn = DiceLoss()

[0313] def forward(self, data):

[0314] return torch.sigmoid(self.model(data))

[0315] def training_step(self, batch, batch_idx):

[0316] mri, mask = batch

[0317] mask = mask.float()

[0318] pred = self(mri)

[0319] loss = self.loss_fn(pred, mask) self.training_step_outputs.append(loss)

[0320] self.log("Training Dice", loss)

[0321] if batch_idx % 50 ==0 :

[0322] self.log_images(mri.cpu() , pred.cpu() , mask.cpu() , "Train" ) return loss def study_list_view(request):

[0323] """ API endpoint for OHIF study list"""

[0324] try:

[0325] studies = DICOMWeb.objects.values(

[0326] 'study_uid', 'patient_id', 'patient_name', 'study_date', 'modality'

[0327] ).distinct()

[0328] study_list = []

[0329] for study in studies:

[0330] # Get series count for this study

[0331] series_count = DICOMWeb.objects.filter(

[0332] study_uid=study['study_uid']

[0333] ). values('series_uid'). distinct(). count()

[0334] # Get instance count for this study

[0335] instance_count = DICOMWeb.objects.filter(

[0336] study_uid=study['study_uid']

[0337] ).count()

[0338] study_list.append({

[0339] 'StudyInstanceUID': study['study_uid'],

[0340] 'PatientID': study['patient_id'],

[0341] 'PatientName': study['patient_name'],

[0342] 'StudyDate': study['study_date'].strftime('%Y-%m-%d') if study['study_date'] else ", 'Modalities': study['modality'],

[0343] 'SeriesCount': series_count,

[0344] 'InstanceCount': instance_count

[0345] })

[0346] return JsonResponse(study_list, safe=False)

[0347] class WADORSView(APIView):

[0348] """ WADO-RS implementation for DICOMweb """

[0349] def get(self, request, study_uid, series_uid, instance_uid):

[0350] try:

[0351] # Retrieve the instance

[0352] instance = DICOMWeb.objects.get(

[0353] study_uid=study_uid,

[0354] series_uid=series_uid,

[0355] instance_uid=instance_uid

[0356] )

[0357]

[0358] dicom_file_path_full = os.path.join(settings.MEDIA_ROOT, instance.dicom_file_path) print(dicom_file_path_full)

[0359] # Check if file exists

[0360] if not os.path.exists(dicom_file_path_full):

[0361] return Response(

[0362] {"error": " DICOM file not found"},

[0363] status=status.HTTP_404_NOT_FOUND

[0364] )

[0365] # Return DICOM file with proper headers

[0366] with open(dicom_file_path_full, 'rb') as f:

[0367] response = HttpResponse(

[0368] f.read(),

[0369] content_type='application / dicom'

[0370] )

[0371] response['Content-Disposition'] = f'inline;

[0372] filename="{os.path.basename(dicom_file_path_full)}"'

[0373] return response

[0374] except DICOMWeb. DoesNotExist:

[0375] return Response(

[0376] {"error": " DICOM instance not found"},

[0377] status=status.HTTP_404_NOT_FOUND

[0378] )

[0379] except Exception as e:

[0380] logger. error(f" WADO-RS error: {str(e)}")

[0381] return Response(

[0382] {"error": "Internal server error"}, status=status.HTTP_500_INTERNAL_SERVER_ERROR

[0383] )

[0384] class WADORSMetadataView(APIView):

[0385] """ WADO-RS Metadata endpoint"""

[0386] def get(self, request, study_uid, series_uid, instance_uid):

[0387] try:

[0388] instance = DICOMWeb.objects.get(

[0389] study_uid=study_uid,

[0390] series_uid=series_uid,

[0391] instance_uid=instance_uid

[0392] )

[0393] # Convert metadata to DICOMweb JSON format

[0394] metadata = instance.metadata response_data = [{

[0395] "00080018": {"vr": " UI", " Value": [instance. instance_uid]}, "00080016": {"vr": " UI", " Value": [metadata.get('SOPClassUID', ")]}, "00080060": {"vr": " CS", " Value": [instance.modality]},

[0396] "00100020": {"vr": " LO", " Value": [instance.patient_id]},

[0397] "00100010": {"vr": " PN", " Value": [{" Alphabetic": instance.patient_name}]}, "0020000D": {"vr": " UI", " Value": [instance. study_uid]},

[0398] "0020000E": {"vr": " UI", " Value": [instance.series_uid]},

[0399] "00200013": {"vr": " IS", " Value": [metadata.get('InstanceNumber', ")]}, "00280010": {"vr": " US", " Value": [int(metadata.get('Rows', 0))]}, "00280011": {"vr": " US", " Value": [int(metadata.get('Columns', 0))]}, }]

[0400] return Response(response_data, status=status. HTTP_200_OK)

[0401] except DICOMWeb. DoesNotExist:

[0402] return Response(

[0403] {"error": " DICOM instance not found"},

[0404] status=status.HTTP_404_NOT_FOUND

[0405] )

[0406] except Exception as e:

[0407] logger.error(f" WADO-RS Metadata error: {str(e)}")

[0408] return Response(

[0409] {"error": "Internal server error"}, status=status.HTTP_500_INTERNAL_SERVER_ERROR

[0410] )

[0411] def create_wsi _placeholder_tile(slide, x, y, width, height, target_w, target_h, format): """ Create informative placeholder tile for WSI files"""

[0412] img = Image.new('RGB', (target_w, target_h), color=(245, 245, 245))

[0413] draw = ImageDraw. Draw(img)

[0414] # Draw grid pattern

[0415] grid_size = min(64, target_w / / 4, target_h / / 4)

[0416] grid_color = (220, 220, 220)

[0417] for i in range(0, target_w, grid_size):

[0418] draw.line([(i, 0), (i, target_h)], fill=grid_color, width=1)

[0419] for i in range(0, target_h, grid_size):

[0420] draw.line([(0, i), (target_w, i)], fill=grid_color, width=1)

[0421] # Add border

[0422] draw.rectangle([0, 0, target_w - 1, target_h - 1], outline=(180, 180, 180), width=2) try:

[0423] # Try to use better fonts

[0424] try:

[0425] font_large = ImageFont.truetype("arial.ttf", 16)

[0426] font_small = ImageFont.truetype("arial.ttf", 12)

[0427] font_xsmall = ImageFont.truetype("arial.ttf", 10)

[0428] except:

[0429] font_large = ImageFont.load_default()

[0430] font_small = ImageFont.load_default()

[0431] font_xsmall = ImageFont.load_default()

[0432] center_x = target_w / / 2

[0433] # Title

[0434] draw.text((center_x, target_h / / 2 - 40), " Whole Slide Image",

[0435] fill=(0, 100, 200), font=font_large, anchor="mm")

[0436] # File info

[0437] filename_short = slide.filename[:25] + "..." if len(slide.filename) > 25 else slide.filename draw.text((center_x, target_h / / 2 - 15), filename_short,

[0438] fill=(80, 80, 80), font=font_small, anchor="mm")

[0439] # Installation instructions

[0440] instructions = [

[0441] " To view this WSI file:",

[0442] "1. Download OpenSlide from openslide.org",

[0443] "2. Extract to C:\\openslide\\",

[0444] "3. Restart the application",

[0445] f'Tile: {target_w}x{target_h}"

[0446] ]

[0447] for i, line in enumerate(instructions):

[0448] draw.text((center_x, target_h / / 2 + 10 + i * 18), line,

[0449] fill=( 150, 150, 150), font=font_xsmall, anchor="mm")

[0450] except Exception as font error:

[0451] # Simple fallback

[0452] draw.text((10, 10), " Whole Slide Image", fill=(0, 100, 200))

[0453] draw.text((10, 30), " Install OpenSlide", fill=( 100, 100, 100))

[0454] draw.text((10, 50), "from openslide.org", fill=( 100, 100, 100))

[0455] return serve_pil_image(img, format)

[0456] def create_placeholder_tile(title, format, message=None):

[0457] """ Create a generic placeholder tile """

[0458] img = Image.new('RGB', (256, 256), color=(240, 240, 240)) draw = ImageDraw. Draw(img)

[0459] draw.rectangle([0, 0, 255, 255], outline=(200, 200, 200), width=2)

[0460] try:

[0461] # Try to use better fonts

[0462] try:

[0463] font_large = ImageFont.truetype("arial.ttf", 14)

[0464] font_small = ImageFont.truetype("arial.ttf", 10)

[0465] except:

[0466] font_large = ImageFont.load_default()

[0467] font_small = ImageFont.load_default()

[0468] # Title

[0469] draw. text(( 128, 80), title, fill=(0, 0, 0), font=font_large, anchor="mm")

[0470] # Message (split into multiple lines if needed)

[0471] if message:

[0472] lines = message. split('\n')

[0473] y_pos = 110

[0474] for line in lines:

[0475] draw.text((128, y_pos), line, fill=( 100, 100, 100), font=font_small, anchor="mm") y_pos += 20

[0476] except Exception:

[0477] # Simple fallback

[0478] draw.text((10, 10), title, fill=(0, 0, 0))

[0479] if message:

[0480] draw.text((10, 30), message[:30], fill=(100, 100, 100))

[0481] return serve_pil_image(img, format)

[0482] def serve _pil_image(pil_img, format):

[0483] """Convert PIL image to HTTP response with optimization"""

[0484] output = io. BytesIO()

[0485] if format.lower() in ['jpg', 'jpeg']:

[0486] # Convert to RGB if necessary for JPEG

[0487] if pil_img.mode in ['RGBA', 'LA', 'P']:

[0488] background = Image.new('RGB', pil_img.size, (255, 255, 255))

[0489] if pil_img.mode == 'P':

[0490] pil_img = pil_img.convert('RGBA')

[0491] background.paste(pil_img, mask=pil_img.split()[-1] if pil_img.mode == 'RGBA' else None) pil_img = background

[0492] pil_img.save(output, format='JPEG', quality=85, optimize=True)

[0493] content_type = 'image / jpeg' else:

[0494] pil_img.save(output, format=format.upper(), optimize=True)

[0495] content_type = fimage / {format.lower()}'

[0496] output.seek(0)

[0497] response = HttpResponse(output.getvalue(), content_type=content_type) response['Cache-Control'] = 'public, max-age=3600' # Cache for 1 hour

[0498] return response

[0499] def iiif_tile(request, slide_id, region, size, rotation, quality, format):

[0500] """ Handle IIIF tile requests with comprehensive WSI support"""

[0501] if not request.user.is_authenticated:

[0502] return HttpResponse('Unauthorized', status=401)

[0503] slide = get_object_or_404(WholeSlideImage, id=slide_id)

[0504] if slide.upload.user!= request. user:

[0505] return HttpResponse('Access denied', status=403)

[0506] slide_path = get_slide_file_path(slide)

[0507] is_wsi = is_whole_slide_image(slide. filename)

[0508] can_use_wsi = can_use_large_image(slide_path, slide. filename)

[0509] if not slide_path or not os.path.exists(slide_path):

[0510] print(f'File not found: {slide_path}')

[0511] return create_placeholder_tile(

[0512] " File Not Available",

[0513] format,

[0514] f'Slide file could not be found.\nPlease re-upload the file.\nSlide: {slide.filename}'

[0515] try:

[0516] # Parse IIIF parameters

[0517] if region == 'full':

[0518] width, height = get_slide_dimensions(slide_path, slide.filename)

[0519] region_x, region_y, region_w, region_h = 0, 0, width, height

[0520] elif region == 'square':

[0521] width, height = get_slide_dimensions(slide_path, slide.filename)

[0522] min_dim = min(width, height)

[0523] region_x, region_y = (width - min_dim) / / 2, (height - min_dim) / / 2

[0524] region w, region h = min dim, min dim

[0525] else:

[0526] region_parts = region.split(',')

[0527] region_x, region_y, region_w, region_h = map(int, region_parts)

[0528] # Handle size if size == 'full':

[0529] target_w, target_h = region_w, region_h

[0530] elif size == 'max':

[0531] target_w, target_h = min(region_w, 2000), min(region_h, 2000)

[0532] elif size.startswith('pct:'):

[0533] scale = float(size[4:]) / 100.0

[0534] target_w = int(region_w * scale)

[0535] target_h = int(region_h * scale)

[0536] elif ',' in size and not size.startswith(','):

[0537] size_parts = size.split(',', ')

[0538] target_w, target_h = map(int, size_parts)

[0539] elif size.startswith(','):

[0540] target_h = int(size[ 1:])

[0541] target_w = int(region

[0542]

[0543] _w * target_h / region_h)

[0544] else:

[0545] target_w = int(size)

[0546] target_h = int(region_h * target_w / region_w)

[0547] # Limit maximum size for performance

[0548] target_w = min(target_w, 4000)

[0549] target_h = min(target_h, 4000)

[0550] print(f'Tile request: region={region}, size={size}, target={target_w}x{target_h}')

[0551] # Try direct OpenSlide first (most reliable for.svs files)

[0552] if OPENSLIDE_AVAILABLE and is_wsi:

[0553] try:

[0554] return serve_openslide_tile(slide_path, region_x, region_y, region_w, region_h, target_w, target_h,

[0555] format)

[0556] except Exception as e:

[0557] print(f'OpenSlide tile failed: {e}')

[0558] # Use large-image for supported files

[0559] if LARGE_IMAGE_AVAILABLE and can_use_wsi:

[0560] try:

[0561] return serve_large_image_tile(slide_path, region_x, region_y, region_w, region_h, target_w, target_h,

[0562] format)

[0563] except Exception as e:

[0564] print(f'Large-image tile failed: {e}')

[0565] # Fall through to alternative methods

[0566] # For WSI files without support, show informative placeholder

[0567] if is_wsi and not can_use_wsi:

[0568] print(f'Showing WSI placeholder for {slide.filename}')

[0569] return create_wsi_placeholder_tile(slide, region_x, region_y, region_w, region_h, target_w, target_h,

[0570] format)

[0571] # Regular image processing with Pillow

[0572] try:

[0573] with Image. open(slide_path) as img:

[0574] img_width, img_height = img. size

[0575] # Ensure region is within image bounds

[0576] region_x = max(0, min(region_x, img_width - 1))

[0577] region_y = max(0, min(region_y, img_height - 1))

[0578] region_w = min(region_w, img_width - region_x)

[0579] region_h = min(region_h, img_height - region_y)

[0580] if region_w <= 0 or region_h <= 0:

[0581] return create_placeholder_tile("Invalid Region", format, "Requested region is outside image bounds")

[0582] # Crop and resize

[0583] tile = img.crop((region_x, region_y, region_x + region_w, region_y + region_h))

[0584] if (tile.width, tile.height)!= (target_w, target_h):

[0585] tile = tile.resize((target_w, target_h), Image. Resampling. LANCZOS)

[0586] return serve_pil_image(tile, format)

[0587] except Exception as img_error:

[0588] return create_placeholder_tile(

[0589] " Image Error",

[0590] format,

[0591] f'Could not process image: {str(img_error)}'

[0592] )

[0593] except Exception as e:

[0594] print(f'IIIF tile error: {e}')

[0595] return create_placeholder_tile("Server Error", format, str(e))

Claims

ClaimsWhat is claimed is:

1. An integrated multimodal artificial intelligence hospital platform comprising:• a multimodal transformer configured to generate unified patient representations from radiology images, pathology whole-slide images, genomic profiles, laboratory data, longitudinal clinical records, and wearable biosensor data;• an autonomous screening interval generator configured to compute patient-specific, dynamically updated screening schedules using calibrated multimodal risk models of non- communicative diseases and temporal disease-evolution forecasts;• a digital-twin oncology engine configured to simulate tumor progression, metastasis probability, toxicity trajectories, survival likelihood, and response to alternative therapy strategies in real time;• an adaptive therapy optimization engine employing reinforcement-learning-based policy evaluation to identify optimal treatment sequences tailored to the simulated digital-twin state;• and a closed-loop clinical orchestration module integrated with hospital information systems for automated care-pathway generation, treatment monitoring, alerting, and workflow execution;• wherein the platform continuously updates all modules with new patient data and autonomously recalibrates screening, diagnostic inference, and therapy recommendations, thereby enabling full-cycle, industrially deployable management.

2. The platform of claim 1, wherein the multimodal transformer performs cross-modal attention fusion within a shared latent space not requiring separate independent models.

3. The platform of claim 1, wherein the multimodal transformer incorporates modality-specific uncertainty modeling to weight heterogeneous inputs.

4. The system of claim 1, wherein the screening interval generator as autonomous screening interval generation integrates polygenic risk scores, environmental exposures, and lifestyle trajectories.

5. The system of claim 1, wherein screening schedules are recalibrated automatically upon ingestion of new imaging, pathology, or laboratory results.

6. The platform of claim 1, wherein the digital twin is continuously updated using real-time physiological signals transmitted from patient wearables.

7. The platform of claim 1, wherein the digital twin generates counterfactual therapy trajectories for prediction of treatment response and toxicity.

8. The system of claim 1, wherein the reinforcement-learning model evaluates competing therapy strategies including surgery, chemotherapy, targeted therapy, immunotherapy, radiotherapy, and combination regimens.

9. The system of claim 1, wherein therapy optimization incorporates drug-interaction constraints, toxicity thresholds, and cost-effectiveness objectives.

10. The system of claim 1, wherein closed-loop monitoring triggers alerts when predicted tumor growth exceeds a model-defined threshold.

11. The system of claim 1, wherein the orchestration module automatically generates clinicianready care pathways aligned with NCCN, ESMO, ASCO and clinical guidelines.

12. The system of claim 1, wherein the platform communicates with hospital PACS, LIS, EMR, and genomic laboratories using HL7-FHIR and DICOM protocols.

13. The system of claim 1, wherein the system automates reporting, tumor-board case summaries, and follow-up recommendations.

14. The platform of claim 1 configured to optimize population-level screening resources using predictive cancer incidence modeling.

15. The platform of claim 1 capable of generating autonomous differential-diagnostic suggestions based on multimodal embeddings.

16. The platform of claim 1 configured to recommend follow-up imaging modality and frequency to minimize cumulative radiation exposure.

17. The system of claim 1 configured to compute individualized chemotherapy dosing using toxicity and pharmacogenomic simulation.

18. The system of claim 1 comprising a module for adaptive radiotherapy planning based on digital-twin tumor-response predictions.

19. The platform of claim 1 incorporating model interpretability layers that generate SHAP-based or attention-based explanations for every automated recommendation.

20. The platform of claim 1 including integrity- verified audit logs tracking all Al-driven clinical actions.

21. The platform of claim 1 supporting federated learning to aggregate oncology intelligence across multiple hospitals without transferring patient-level data.

22. The platform of claim 1 configured to deploy across cloud, on-premise, or hybrid infrastructures.

23. The platform of claim 1 wherein patient outcomes are used to update the reinforcementlearning therapy-optimization policy.

24. The platform of claim 1 wherein screening interval recommendations tighten or relax based on longitudinal digital-twin forecasts.

25. The platform of claim 1 configured to autonomously escalate high-risk cases and generate clinician alerts based on thresholds learned from digital-twin simulations.

Images