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Related Concept Videos

Adjusting a Traverse01:12

Adjusting a Traverse

48
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...
48
Design Example: Traverse Angle Computations01:25

Design Example: Traverse Angle Computations

55
Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
55

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Deep learning algorithm enables automated Cobb angle measurements with high accuracy.

Daichi Hayashi1,2, Nor-Eddine Regnard3,4, Jeanne Ventre4

  • 1Department of Radiology, Chobanian and Avedisian School of Medicine, Boston University, Boston, MA, USA. daichi.alex.hayashi@gmail.com.

Skeletal Radiology
|December 17, 2024
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Summary
This summary is machine-generated.

Deep learning accurately measures the Cobb angle on full spine radiographs for scoliosis patients. This AI tool shows high precision, particularly in pediatric cases, aiding in spinal deformity assessment.

Keywords:
Cobb angleDeep learningRadiographScoliosis

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Area of Science:

  • Radiology
  • Artificial Intelligence in Medicine
  • Spinal Imaging Analysis

Background:

  • Scoliosis management relies on accurate Cobb angle measurement from spinal radiographs.
  • Manual measurement can be time-consuming and subject to inter-observer variability.
  • Deep learning (DL) offers potential for automated and objective radiographic analysis.

Purpose of the Study:

  • To evaluate the accuracy of a deep learning algorithm for automated Cobb angle measurements on full spine radiographs.
  • To compare DL performance against expert manual annotations in a diverse patient cohort.

Main Methods:

  • Full spine radiographs from patients over 2 years old were analyzed.
  • Cobb angles were manually annotated by three expert musculoskeletal radiologists/orthopedic surgeons.
  • Ground truth was established by consensus or agreement among annotators.
  • A deep learning software (BoneMetrics, Gleamer) was used for automated measurements.
  • Accuracy was assessed using Mean Absolute Error (MAE) compared to manual annotations.

Main Results:

  • The study included 345 patients (179 pediatric, 166 adult).
  • The DL algorithm achieved a Mean Absolute Error (MAE) of 2.6° for the main curvature.
  • Pediatric patients showed higher accuracy with an MAE of 1.9°, compared to 3.3° in adults.

Conclusions:

  • The deep learning algorithm demonstrates high accuracy in predicting Cobb angles for scoliotic patients.
  • Automated DL measurements can reliably assess spinal deformities, potentially improving efficiency and consistency in clinical practice.