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In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...

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Related Experiment Video

Updated: Jun 18, 2025

Artificial Intelligence Approaches to Assessing Primary Cilia

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data

Shuzo Kato¹, Yoshihiro Maeda¹, Takeo Nagura¹

¹Department of Orthopedic Surgery, Keio University School of Medicine, Shinanomachi 35, Shinjuku, Tokyo, 160-8582, Japan.

Scientific Reports

|August 3, 2024

Summary

Artificial intelligence (AI) algorithms were developed to measure Cobb angles in spinal deformities like adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD). Training AI on combined AIS and ASD data yielded the most accurate measurements, improving clinical practice.

Keywords:

AI algorithms Adolescent idiopathic scoliosis Adult spinal deformity Cobb angle

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Area of Science:

Orthopedics
Medical Imaging
Artificial Intelligence

Background:

Spinal deformities, such as adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD), are prevalent conditions requiring precise radiographic measurements.
Accurate Cobb angle measurement on coronal radiographs is critical for diagnosing and planning treatment for spinal deformities.

Purpose of the Study:

To develop and evaluate artificial intelligence (AI) algorithms for precise Cobb angle measurement in both AIS and ASD.
To compare the accuracy of AI algorithms trained on combined datasets versus disease-specific datasets.

Main Methods:

Three AI algorithms were developed: one trained on combined AIS/ASD data, one on AIS data only, and one on ASD data only.
A dataset of 1612 whole-spine radiographs (1029 AIS, 583 ASD) was used for training.
Accuracy was assessed on a separate test set of 285 radiographs using mean absolute error (MAE) and intraclass correlation coefficient (ICC) against expert manual measurements.

Main Results:

The AI algorithm trained on both AIS and ASD cases demonstrated superior accuracy compared to disease-specific trained AI.
The AIS/ASD-trained AI achieved the highest accuracy in Cobb angle measurements.

Conclusions:

Training AI models on diverse datasets encompassing multiple related diseases can be an effective strategy for enhancing performance.
The developed AI algorithm shows potential for reducing measurement errors and improving the quality of clinical practice in managing spinal deformities.