Trends in AI-powered classification of thyroid neoplasms based on histopathology images, a systematic review

Abstract

AbstractBackgroundAssessment of thyroid nodules histopathology using AI is crucial for an accurate diagnosis. This systematic review analyzes recent works employing deep learning approaches for classifying thyroid nodules based on histopathology images, evaluating their performance, and identifying limitations.MethodsEligibility criteria focused on peer-reviewed English papers published in the last five years, applying deep learning to categorize thyroid histopathology images. The PubMed database was searched using relevant keyword combinations.ResultsOut of 103 articles, 11 studies met inclusion criteria. They used convolutional neural networks to classify thyroid neoplasm. Most studies aimed at basic tumor subtyping; however, three studies targeted the prediction of tumor-associated mutation. Accuracy ranged from 77% to 100%, with most over 90%.DiscussionThe findings from our analysis reveal the effectiveness of deep learning in identifying discriminative morphological patterns from histopathology images, thus enhancing the accuracy of thyroid nodule histopathological classification. Key limitations were small sample sizes, subjective annotation, and limited dataset diversity. Further research with larger diverse datasets, model optimization, and real-world validation is essential to translate these tools into clinical practice.OtherFundingThis study did not receive any funding.RegistrationThe procedural instructions for this systematic review were officially recorded within the PROSPERO database under registration numberRD42023457854https://www.crd.york.ac.uk/Prospero/