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Updated: Jun 16, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Automated Rigo Classification: An Innovative Artificial Intelligence System.

N Schmidt1, G Johnson1, A Nencka1,2

  • 1Medical College of Wisconsin.

Studies in Health Technology and Informatics
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) model was developed to standardize the Rigo classification system (RCS) for adolescent idiopathic scoliosis (AIS). While faster than human observers, the AI model showed lower accuracy, highlighting potential for future improvements in scoliosis treatment.

Keywords:
Rigo classificationaccuracyartificial intelligenceidiopathic scoliosisradiography

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Area of Science:

  • Orthopedics
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Clinician application of the Rigo classification system (RCS) for adolescent idiopathic scoliosis (AIS) lacks standardization.
  • No existing artificial intelligence (AI) models specifically address RCS standardization for AIS.

Purpose of the Study:

  • To develop an AI model for applying the RCS to X-rays.
  • To compare the AI model's parameter measurements, processing time, and classification accuracy against human observers.

Main Methods:

  • An open-source AI model from Scoliosis Tools was adapted to apply the RCS.
  • Twenty PA-view X-rays of AIS patients were analyzed by the AI model and three human observers.
  • Processing time, RCS parameter measurements, and classification accuracy were recorded and compared.

Main Results:

  • The AI model achieved RCS classification in 14.6 seconds, significantly faster than human observers (37.1 seconds).
  • No significant differences were found between AI and human measurements of RCS parameters (P>0.05).
  • The AI model demonstrated 60.0% accuracy, compared to 75.0% for human observers.

Conclusions:

  • The developed AI model offers a high-speed tool for standardizing RCS classification in AIS.
  • AI-driven RCS parameter measurement shows potential for consistent clinical application.
  • Future AI iterations may enhance treatment planning efficiency for scoliosis, including exercise and bracing.