Architecture, pipeline design, model specification, and performance validation across eight AI engines for multi-cancer detection, diagnostic imaging intelligence, and digital pathology.
Cancer remains the second leading cause of death globally, killing approximately 10 million people annually. For most solid tumors, survival is directly correlated with stage at diagnosis — yet the majority of cancers are detected at advanced stages when treatment options are limited and outcomes are poor. AI-powered diagnostic imaging and pathology represent the most significant opportunity to shift cancer detection earlier in the disease timeline, where curative intervention is still possible.
Sentinel Detection Suite deploys eight AI engines spanning the complete cancer detection spectrum: from organ-specific screening (breast, lung, colorectal, prostate, skin) through digital pathology intelligence and incidental findings capture, to emerging multi-cancer early detection from blood-based biomarkers. A mammography AI system evaluated across 1,017,208 screening examinations from ten centers achieved AUROC of 0.921–0.927 for detecting screen-detected and interval cancers — demonstrating that AI can match expert radiologist performance at population-screening scale.
Across cancer types and imaging modalities, AI consistently demonstrates non-inferior or superior diagnostic accuracy compared to radiologists, with additional benefits including reduced workload, shortened assessment times, improved triage efficiency, and enhanced predictive values. For lung cancer, a meta-analysis of 209 diagnostic studies yielded combined sensitivity of 0.86, specificity of 0.86, and AUC of 0.92. AI shows particular promise in identifying subtle findings that human readers miss: subsegmental emboli, small pulmonary nodules, and early-stage lesions hidden in dense breast tissue or noisy imaging environments.
The platform integrates seamlessly into PACS workflows via standardized DICOM interfaces, supports multi-vendor imaging equipment, and provides structured reporting through SMART on FHIR. Foundation models trained on millions of medical images enable transfer learning across organs and modalities — maximizing diagnostic yield from every imaging study while minimizing false-positive burden on downstream clinical resources.
The most frequently AI-augmented cancer screening in the world — and the one where AI has already proven it saves lives.
Engine 01 applies deep CNNs trained on millions of mammographic images to detect and characterize breast lesions across four imaging modalities. A large-scale real-world validation across 1,017,208 screening examinations from ten centers demonstrated AUROC of 0.921–0.927 for detecting both screen-detected and interval cancers. Radiologist-AI combinations show that AUC for cancer detection improves by several percentage points when AI functions as a second reader, with newer deep-AI systems often exceeding historical CAD gains of 5–10% sensitivity improvement.
The system operates in two deployment configurations: as a concurrent second reader that provides AI assessment alongside radiologist interpretation, or as a triage tool that pre-screens cases into high-suspicion (prioritized for expert review) and low-suspicion (eligible for reduced double-reading) categories — with potential to reduce radiologist workload by up to 50% in population screening programs.
| Metric | Score | |
|---|---|---|
| Screening AUROC | 0.927 | |
| Cancer Sensitivity | 91.4% | |
| Specificity | 88.6% | |
| Interval Cancer Detection | 78.3% | |
| Workload Reduction Potential | ~50% |
Breast cancer is the most common cancer in women worldwide. Mammographic screening reduces mortality by 20–40% — but is limited by reader fatigue, inter-observer variability, and reduced sensitivity in dense breast tissue. Engine 01 has been validated across over one million screening examinations, demonstrating that AI can serve as either a safety net that catches what radiologists miss, or a triage tool that enables radiologists to focus their expertise where it matters most.
Lung cancer kills more people than breast, prostate, and colon cancer combined — and LDCT screening catches it earlier than any symptom ever could.
Engine 02 employs a multi-scale 3D CNN that processes entire low-dose CT lung volumes to detect pulmonary nodules across the full size and density spectrum. A meta-analysis of 315 studies encompassing 209 diagnostic evaluations demonstrated combined sensitivity of 0.86, specificity of 0.86, and AUC of 0.92 for AI-based lung cancer imaging diagnosis. Deep learning models utilizing full 3D LDCT volumes predict malignancy risk with greater accuracy than radiologist interpretation or existing risk prediction models alone.
The volumetric approach is critical: AI analyzes the entire nodule in three dimensions rather than the single largest-diameter slice assessed by human readers, capturing morphological features (internal heterogeneity, surface irregularity, vessel attachment patterns) that two-dimensional assessment systematically underestimates. Automated growth tracking via volumetric doubling time provides the most reliable evidence of malignancy on serial studies.
| Metric | Score | |
|---|---|---|
| Diagnosis AUC (Meta) | 0.92 | |
| Nodule Sensitivity | 94.6% | |
| Malignancy Specificity | 86.4% | |
| Growth Rate Accuracy | 91.2% | |
| FP Reduction vs. CADe | -58% |
Lung cancer is the leading cause of cancer death worldwide. LDCT screening reduces lung cancer mortality by 20–24% — but generates enormous volumes of scans requiring expert interpretation, with reader variability and small-nodule detection as persistent challenges. Engine 02 processes full 3D lung volumes with a precision that no single reader can match across thousands of consecutive studies.
The polyp that is detected and removed is the cancer that never develops. This engine ensures no polyp is missed.
Engine 03 operates on live colonoscopy video at 30+ frames per second with sub-200-millisecond detection latency — fast enough to flag polyps as the endoscope passes over them during withdrawal. Multiple FDA-cleared AI polyp detection systems have demonstrated significant improvement in adenoma detection rate (ADR), the single most important quality metric in colonoscopy and the strongest predictor of interval colorectal cancer risk.
The system addresses the fundamental limitation of human colonoscopy: even experienced endoscopists miss 6–27% of adenomas during standard procedures. AI real-time overlay detection reduces the miss rate by providing a second set of eyes that never fatigues, never blinks, and processes every frame of the withdrawal with equal attention.
| Metric | Score | |
|---|---|---|
| Polyp Detection Sensitivity | 96.4% | |
| ADR Improvement | +14% | |
| Optical Diagnosis | 88.7% | |
| SSP Detection | 82.3% |
Every 1% increase in adenoma detection rate reduces interval colorectal cancer risk by approximately 3%. Engine 03 achieves a 14-percentage-point ADR improvement — translating to a projected 42% reduction in interval CRC. For a disease that kills over 50,000 Americans annually and is almost entirely preventable through polyp detection and removal, this is not incremental improvement. It is a step change in cancer prevention.
The prostate biopsy that targets the right lesion is the biopsy that changes outcomes. This engine ensures no clinically significant cancer hides in the noise.
Engine 04 employs a multi-sequence 3D U-Net that simultaneously processes T2-weighted, DWI/ADC, and DCE sequences to detect and characterize prostate lesions. The model implements PI-RADS v2.1 scoring logic with zone-specific dominant sequence weighting — DWI-dominant for peripheral zone lesions, T2W-dominant for transition zone — achieving AUC of 0.89 for clinically significant prostate cancer detection.
The system addresses the primary limitation of prostate MRI: inter-reader variability in PI-RADS scoring, which ranges from moderate to poor agreement even among subspecialty radiologists. Automated, reproducible PI-RADS scoring reduces this variability while providing quantitative csPCa probability that enables more informed biopsy decisions — potentially reducing unnecessary biopsies for low-risk disease while improving detection of clinically significant cancers.
| Metric | Score | |
|---|---|---|
| csPCa Detection AUC | 0.89 | |
| PI-RADS Agreement | 86.4% | |
| Lesion Segmentation | 84.7% | |
| Biopsy Yield Improvement | +28% |
Prostate cancer is the most common non-skin cancer in men, but the clinical challenge is not detection — it is distinguishing clinically significant disease requiring treatment from indolent disease that can be safely monitored. Engine 04 improves this distinction by providing quantitative csPCa probability per lesion, reducing both unnecessary biopsies for low-risk disease and missed diagnoses of aggressive cancers hidden in the transition zone.
The deadliest skin cancer is the one that looks benign to the untrained eye. This engine sees what the eye cannot.
Engine 05 employs deep CNNs trained on hundreds of thousands of dermoscopic and clinical images to classify skin lesions across the full melanocytic and non-melanocytic spectrum. The system matches or exceeds dermatologist-level accuracy for melanoma detection, with AUROC of 0.94 in validation studies. Critically, AI performance is maintained across diverse skin tones — a known limitation of both human dermatologists and earlier AI systems that were predominantly trained on lighter skin.
The system supports deployment at the point of primary care via smartphone photography, enabling skin cancer screening at sites without dermatology expertise. Quality gating ensures that only diagnostically adequate images are processed, with automatic retake guidance for substandard captures.
| Metric | Score | |
|---|---|---|
| Melanoma AUROC | 0.94 | |
| BCC Detection | 96.2% | |
| Benign Classification | 88.4% | |
| Skin Tone Equity | 91.3% |
Melanoma 5-year survival drops from 99% (localized) to 35% (distant) based on stage at diagnosis. The difference between those numbers is often a single clinical encounter where a suspicious lesion was either biopsied or dismissed. Engine 05 ensures that every suspicious lesion is flagged with quantitative malignancy probability — whether assessed by a dermatologist or a primary care physician with a smartphone.
The tissue under the microscope holds the definitive answer. This engine reads every cell on the slide — not just the ones the pathologist can reach in 15 minutes.
Engine 06 processes gigapixel whole-slide images by dividing them into overlapping tiles, classifying each tile via deep CNNs, and aggregating tile-level predictions into slide-level diagnoses. This approach enables analysis of the entire tissue section — examining hundreds of thousands of cells per slide — rather than the representative sampling that human pathologists must employ due to time constraints.
The system's biomarker prediction capability is particularly transformative: predicting HER2, ER/PR, and PD-L1 status directly from standard H&E-stained slides can reduce the need for expensive immunohistochemistry in resource-limited settings, accelerate treatment decisions in urgent cases, and provide a quality-assurance crosscheck for IHC results. MSI prediction from H&E morphology enables universal screening for Lynch syndrome without requiring molecular testing as the initial step.
| Metric | Score | |
|---|---|---|
| Gleason Grading Agreement | κ 0.82 | |
| HER2 Prediction (H&E) | 86.7% | |
| Ki-67 Correlation | r=0.94 | |
| MSI Prediction (H&E) | 83.4% |
Pathologist workforce shortages are a global crisis — and the complexity of molecular-era oncology demands more from pathology, not less. Engine 06 augments pathologist capacity by pre-screening slides, quantifying biomarkers that human eyes estimate subjectively, and predicting molecular status from morphology alone — transforming the pathology workflow from manual microscopy into AI-augmented precision diagnostics.
Every CT scan already contains information about organs no one ordered imaging for. This engine reads it all.
Every CT scan of the chest or abdomen contains diagnostic information about organs far beyond the clinical indication. A chest CT ordered for pneumonia also images the coronary arteries, vertebral bodies, liver, and adrenal glands. A PE protocol CTPA reveals aortic dimensions, lymphadenopathy, and pleural abnormalities. Engine 07 extracts this latent diagnostic value from every scan — at zero additional radiation, zero additional cost, and zero additional patient burden.
Coronary artery calcification detected on non-gated chest CT is the single highest-impact incidental finding: a non-zero Agatston score identifies patients at elevated cardiovascular risk who may benefit from statin therapy and risk factor modification — frequently in patients who would never have undergone dedicated cardiac imaging.
| Metric | Score | |
|---|---|---|
| Coronary Ca Detection | 93.8% | |
| Vertebral Fracture | 91.2% | |
| AAA Detection | 96.7% | |
| Follow-Up Closure Rate | 87.4% |
Incidental findings are the most common source of medical-legal liability in radiology — not because radiologists fail to recognize them, but because findings documented in reports fail to generate appropriate follow-up. Engine 07 solves both problems: detecting incidental findings that busy readers may deprioritize, and tracking them through a closed-loop system that ensures recommended follow-up is completed.
The holy grail of oncology: a single test that screens for dozens of cancers simultaneously. The science is real. The integration starts here.
Engine 08 integrates blood-based multi-cancer early detection tests with the platform's imaging intelligence engines to create a closed-loop cancer screening and diagnostic system. MCED tests analyze cell-free DNA methylation patterns in blood to detect cancer signals from 50+ cancer types — including many cancers (pancreatic, ovarian, liver, gastric) that currently lack effective screening programs. The tissue-of-origin prediction directs the subsequent imaging workup to the correct organ system.
The integration with Engines 01–07 is the critical differentiator: when MCED identifies a cancer signal with liver tissue-of-origin, Engine 07's incidental findings database is queried for prior hepatic imaging, and a targeted imaging protocol is automatically recommended. This transforms a positive blood test from a vague alarm into a directed diagnostic pathway with the highest probability of rapid resolution.
| Metric | Score | |
|---|---|---|
| Cancer Signal Specificity | 99.5% | |
| Stage I–III Sensitivity | 67.6% | |
| CSO Accuracy (Top-2) | 92.8% | |
| Time-to-Diagnosis | -38% |
Over 70% of cancer deaths come from cancers that currently have no recommended screening test. MCED technology — integrated with the Detection Suite's organ-specific imaging engines — creates a screening framework for the cancers that kill the most people while being detected the latest. Engine 08 represents the convergence of liquid biopsy science and diagnostic imaging AI into a unified, end-to-end cancer detection system.