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Area Problem01:26

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Determining the area of a region with straight edges is straightforward, as geometric formulas for rectangles, triangles, and polygons can be applied directly. However, traditional geometric methods are insufficient when a region has a curved boundary, such as the area under a function.fromThe area problem involves finding a systematic way to measure such regions. One approach to solving this problem is through approximation. Instead of attempting to compute the area exactly at the outset, the...

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The Goeckerman Regimen for the Treatment of Moderate to Severe Psoriasis
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Measurement of Body Surface Area for Psoriasis Using U-net Models.

Yih-Lon Lin1, Adam Huang2, Chung-Yi Yang3,4

  • 1Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan.

Computational and Mathematical Methods in Medicine
|February 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a U-net deep learning model for automated psoriasis lesion segmentation and body surface area (BSA) measurement. The AI model achieved dermatologist-level accuracy, improving objective disease severity assessment.

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate psoriasis body surface area (BSA) measurement is vital for assessing disease severity and guiding treatment.
  • Subjective visual evaluation by physicians lacks reliability for precise BSA assessment.
  • Objective and automated methods are needed for consistent psoriasis severity evaluation.

Purpose of the Study:

  • To develop and evaluate a machine learning model for automated psoriasis lesion segmentation and BSA measurement.
  • To assess the performance of a U-net based artificial neural network for this task.
  • To provide an objective tool for evaluating psoriasis severity.

Main Methods:

  • A U-net convolutional neural network architecture was employed for psoriasis lesion segmentation.
  • The model was trained on 255 high-resolution images of psoriasis lesions.
  • Performance was evaluated using metrics like average residual and interclass correlation coefficient against dermatologist assessments.

Main Results:

  • The U-net model demonstrated high accuracy in segmenting psoriasis lesions and estimating BSA.
  • The average residual between predicted and ground truth BSA was approximately 0.033.
  • An interclass correlation coefficient of 0.966 indicated strong agreement with dermatologist segmentations.

Conclusions:

  • The proposed U-net model achieves dermatologist-level performance in estimating psoriasis-involved BSA.
  • Automated segmentation and BSA measurement using deep learning offer a reliable alternative to subjective visual assessment.
  • This AI-driven approach can enhance objective evaluation of psoriasis severity.