{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T23:24:14Z","timestamp":1791588254861,"version":"4.3.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Thyroid\u00ae"],"published-print":{"date-parts":[[2020,6]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>Ultrasound images of thyroid nodules from patients who underwent either biopsy or thyroid surgery from February 2012 to February 2017 in our institution were used to create AI models. Nodules were excluded if there was no definitive diagnosis of it being benign or malignant. A total of 482 nodules met the inclusion criteria and all available images from these nodules were used to create the AI models. Later, these AI models were used to test 103 thyroid nodules that underwent biopsy or surgery from March 2017 to July 2018.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Negative predictive value (NPV) of the image similarity model was 93.2%. Sensitivity, specificity, positive predictive value (PPV), and accuracy of the model were 87.8%, 78.5%, 65.9%, and 81.5%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>When compared with published results of ultrasound thyroid cancer risk stratification systems, our image similarity model had comparable NPV with better sensitivity, specificity, and PPV. By using image similarity AI models, we can decrease subjectivity and decrease the number of unnecessary biopsies. Using image similarity AI model, we were able to create an explainable AI model that increases physician's confidence in the predictions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/thy.2019.0752","type":"journal-article","created":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T07:39:39Z","timestamp":1580801979000},"page":"878-884","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":76,"title":["AIBx, Artificial Intelligence Model to Risk Stratify Thyroid Nodules"],"prefix":"10.1177","volume":"30","author":[{"given":"Johnson","family":"Thomas","sequence":"first","affiliation":[{"name":"Department of Endocrinology, Mercy Hospital, Springfield, Missouri, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tracy","family":"Haertling","sequence":"additional","affiliation":[{"name":"Mercy Research Department, Springfield, Missouri, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2020,6,5]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2018-01933"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2008.0354"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2008-1724"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2015.0020"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1634\/theoncologist.2019-0362"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.7863\/jum.2009.28.12.1685"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2018-01674"},{"key":"e_1_3_5_9_2","volume-title":"Fine-needle aspiration biopsy of the thyroid gland","author":"Dean DS","year":"2015","unstructured":"Dean DS, , Gharib H. 2015. Fine-needle aspiration biopsy of the thyroid gland. 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The transverse ultrasonogram of thyroid papillary carcinoma has a better prediction accuracy than the longitudinal one. IEEE Access, PP(99):1\u20131.","journal-title":"IEEE Access"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41574-019-0204-8"},{"key":"e_1_3_5_20_2","unstructured":"Khan CE 2019 Radiology: Artificial Intelligence. Explainable AI. Available at https:\/\/pubs.rsna.org\/page\/ai\/blog\/2019\/8\/explainable_ai (accessed March3 2020)."},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.1309\/AJCPPHLWMI3JV4LA"},{"key":"e_1_3_5_22_2","doi-asserted-by":"crossref","unstructured":"He K Zhang X Ren S Sun J. 2016 Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 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