DeepScience Tech's Radiology AI module uses deep learning models trained on Indian patient data to assist radiologists with abnormality detection — chest X-rays, CT scans, MRI studies, and fundus images. Integrated with RIS/PACS and EMR for seamless clinical workflow.
India faces a severe radiologist shortage — with an estimated 12,000 radiologists serving a population of 1.4 billion. This means long reporting queues, delayed diagnosis, and missed findings especially in tier 2 and tier 3 cities. DeepScience Tech's Radiology AI acts as a first-read assistant — prioritising critical findings, triaging the worklist, and flagging abnormalities for radiologist review.
Pneumonia, pleural effusion, cardiomegaly, pulmonary oedema, pneumothorax, TB (active and old), hilar lymphadenopathy, and mass lesion detection. Sensitivity 99.2%, specificity 94.7%.
Intracranial haemorrhage (all types), midline shift, cerebral oedema, ischaemic stroke (early signs), and hydrocephalus — with auto-priority flagging for emergency cases.
Diabetic retinopathy grading (DR0–DR4), diabetic macular oedema, glaucoma suspect detection, and age-related macular degeneration — enabling diabetic eye screening at scale.
Fracture detection on X-rays, osteoporosis screening (bone density estimation from X-ray), and joint space narrowing quantification for arthritis staging.
BIRADS classification (0–6), mass and calcification detection, density assessment, and asymmetry flagging — with heat map overlay showing regions of concern.
Cardiomegaly detection, pericardial effusion on echo, LV ejection fraction estimation from 2D echo, and coronary artery calcium scoring from non-contrast CT.
When a new study is acquired, DICOM images are automatically routed to the AI engine via DICOM DIMSE or DICOMweb. The AI processes the study, generates findings in structured format (FHIR), and places the AI report alongside the images in the radiologist's worklist — flagging abnormals for priority review. The radiologist reviews, edits if needed, and signs the final report. The AI findings are stored separately from the radiologist's report for audit and performance tracking.
DeepScience Tech's Radiology AI is particularly impactful for hospitals in tier 2 and tier 3 cities that lack on-site radiologists. The AI provides an immediate first read for all studies, escalates critical findings to an on-call radiologist via mobile alert, and facilitates remote reporting by tele-radiologists through our secure cloud PACS.
We'll demonstrate chest X-ray AI and CT head processing on de-identified sample images from your modality types. Book a 45-minute technical demo.