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Medical Imaging AI

AI-assisted medical imaging systems covering CT, MRI, pathology, and other multimodal imaging for intelligent analysis and structured report generation.

Overview

Medical imaging AI is one of Simo’s core business lines. Based on deep learning, we build intelligent analysis systems for medical institutions covering CT, MRI, X-ray, pathology, and other multimodal imaging — enabling automatic lesion detection, quantitative analysis, and structured report generation to help radiologists and clinicians improve diagnostic speed and accuracy.

Core Capabilities

1. Multimodal Imaging Analysis

  • CT: lung-nodule detection, fracture recognition, intracranial hemorrhage screening, vascular calcification analysis
  • MRI: brain-structure segmentation, tumor-boundary delineation, joint-lesion analysis
  • X-ray/DR: chest-lesion screening, bone-age assessment, scoliosis measurement
  • Pathology: cell classification and counting, tissue-region segmentation, tumor-grading assistance

2. Intelligent Lesion Detection

  • Deep-learning object detection and semantic segmentation models
  • Automatic lesion localization, contour delineation, and key-diameter measurement
  • Multi-phase comparison (pre/post contrast, follow-up comparisons)
  • Simultaneous recognition of calcifications, nodules, masses, and other lesion types

3. Quantitative Analysis Engine

  • Automatic computation of dozens of quantitative metrics (lesion volume, density, morphology, etc.)
  • Automatic tumor assessment per RECIST 1.1 (target-lesion measurement and response evaluation)
  • Automatic registration and change comparison of multi-timepoint follow-up imaging
  • Body-composition analysis (organ volume, fat/muscle area, etc.)

4. Structured Report Generation

  • Automatic drafting of structured reports based on imaging analysis results
  • Automatic key-image capture and embedding into reports
  • Support for ACR BI-RADS, Lung-RADS, LI-RADS, and other standard report templates
  • Ready for signing after physician review and revision

Delivery Process

  1. Requirements analysis: clarify imaging types, lesion types, and clinical workflow
  2. Data preparation: assist with imaging de-identification, annotation, and quality control
  3. Model development: domain fine-tuning on large-scale pretrained models
  4. System integration: connect to hospital PACS and embed into the radiology workstation
  5. Validation & launch: retrospective validation + prospective pilot + formal deployment

Technical Advantages

Dimension Traditional imaging diagnosis Simo Imaging AI
Detection efficiency Frame-by-frame manual reading AI full-image scan + annotation in seconds
Quantification accuracy Visual estimation Pixel-level precise measurement
Follow-up comparison Manually retrieving history Automatic registration + change quantification
Report generation Fully manual writing AI-generated structured draft

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