The Application of Artificial Intelligence in Detecting Breast Lesions with Medical Imaging: A Literature Review

Abstract

Breast cancer is considered the most commonly diagnosed cancer among women worldwide. Several studies have shown that mammography screening could significantly decrease breast cancer mortality. Despite other screening modalities, such as MRI and ultrasound (US), mammography plays a vital role in detecting cancer and following up on it, due to its qualities and properties. The aim of this literature review is to look at recent studies that use AI with different medical imaging mammograms, MRI, and US, in detecting breast lesions. A literature search was carried out using Google Scholar, Semantic Scholar, medRxiv, and PubMed databases for a period of the last four years. The search terms were "breast lesion," "breast imaging," and "breast cancer" combined with "machine learning," "deep learning," and "artificial intelligence." Among these studies, only the medical imaging related to breast lesions with AI was selected. A total of 25 articles were extracted from the following databases: 4 Google Scholar, 3 Semantic Scholar, 4 medRxiv, and 14 PubMed. Only papers related to breast lesions with medical imaging modalities were extracted, and all duplications were removed. In this study, the papers were reviewed by medical imaging professionals. This literature review summarizes the most recent articles on utilizing AI in detecting breast lesions for different imaging modalities: mammogram, ultrasound, and MRI. Reviewed studies showed that AI performance in detecting lesions was significant, associated with high accuracy, sensitivity, and specificity for these modalities.

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