Background: Current classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivity in medical image interpretation. One out of 2 women over the age of 50 years may have a thyroid nodule and at present the only way to exclude malignancy is through invasive procedures for those that are suspicious on ultrasonography. Hence, there exists a need for noninvasive objective classification of thyroid nodules. Some cancers have benign appearance on ultrasonogram. Hence, we decided to create an image similarity algorithm rather than image classification algorithm.

Initial internal validation study in the United States. PubMed link

National and international presentations

2019 Annual meeting of American Thyroid Association. Invited oral podium presentation.

2020 Thyroid, Head and Neck Cancer (THANC) Foundation, virtual journal club.

2020 University of Arizona College of Medicine Phoenix/ Banner University Medical Center-Phoenix Division of Endocrinology Grand Rounds.

2021 European Society of Endocrinology international meeting eECE 2021. Selected for oral presentation.

Pitch competition

Selected for pitch competition at Harvard Catalyst TRANSCEND program. 2021 cohort.


  • Thomas, J., & Haertling, T. (2020). AIBx, artificial intelligence model to risk stratify thyroid nodules. Thyroid.

  • Swan,K, Thomas,J, Nielsen,V, Jespersen,M & Bonnema, S. External validation of AIBx, an artificial intelligence model for risk stratification, in surgically resected thyroid nodules

Other articles and book chapters referencing AIBx:

  • Orloff, L. A. (2020). Artificial Intelligence plus Human Interpretation for Thyroid Nodule Risk Stratification: An Image Similarity Model Keeps the Physician in the Loop. Clinical Thyroidology, 32(6), 276-278.

  • Unnikrishnan, A. G., & Kalra, S. (2020). Could artificial intelligence help in the risk stratification of thyroid nodules?. Thyroid Research and Practice, 17(2), 51.

  • Thomas, J. (2020). Application of Artificial Intelligence in Thyroidology. Artificial Intelligence: Applications in Healthcare Delivery, 273.

  • Thomas, J., Ledger, G. A., & Mamillapalli, C. K. (2020). Use of artificial intelligence and machine learning for estimating malignancy risk of thyroid nodules. Current Opinion in Endocrinology, Diabetes and Obesity, 27(5), 345-350.

  • Wang, S., Xu, J., Tahmasebi, A., Daniels, K., Liu, J. B., Curry, J., ... & Eisenbrey, J. R. (2020). Incorporation of a Machine Learning Algorithm With Object Detection Within the Thyroid Imaging Reporting and Data System Improves the Diagnosis of Genetic Risk. Frontiers in Oncology, 10, 2481.

  • 李铃睿, 杜博, & 陈创. (2020). 人工智能在甲状腺癌精准化诊疗中的研究进展.

  • For up to date citing articles click here

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