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A systematic review on artificial intelligence techniques for detecting thyroid diseases.

Lerina Aversano¹, Mario Luca Bernardi¹, Marta Cimitile²

¹Department of Engineering, University of Sannio, Benevento, Italy.

Peerj. Computer Science

|June 22, 2023

Summary

This summary is machine-generated.

Artificial intelligence (AI) aids early thyroid disease detection, achieving high accuracy. However, challenges include using private, outdated datasets and a need for diverse data types in AI applications for thyroid health.

Keywords:

Artificial intelligence Deep learning Detection Machine learning Thyroid disease

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare

Background:

Artificial intelligence (AI) applications in healthcare are expanding, with early disease detection being a primary focus.
Thyroid diseases, such as hypothyroidism and hyperthyroidism, benefit significantly from early diagnosis for effective treatment and reduced healthcare costs.

Purpose of the Study:

To systematically review and analyze artificial intelligence techniques for detecting and identifying thyroid gland diseases.
To classify reviewed contributions, highlighting the pros and cons of recent research in the field.

Main Methods:

A systematic literature review was conducted, selecting and analyzing 72 papers.
Papers were analyzed based on three research questions: thyroid diseases detected, datasets used, and data types employed.
Contributions were classified using various viewpoints and taxonomies.

Main Results:

The majority of reviewed papers utilize supervised learning methods for detecting hypothyroidism and hyperthyroidism.
The average detection accuracy reported is high, at 96.84%.
Common issues include the use of private, outdated datasets, predominantly featuring clinical data.

Conclusions:

AI demonstrates high potential for accurate thyroid disease detection, particularly for hypo- and hyperthyroidism.
Future research should address the limitations of current datasets and explore diverse data modalities for AI-driven thyroid diagnostics.
Further development is needed to optimize AI applications for broader thyroid disease detection and management.