Artificial Intelligence Applied to Breast MRI for Improved Diagnosis

Jiang, Yulei; Edwards, Alexandra; Newstead, Gillian M.

doi:10.1148/radiol.2020200292

Cited by 91 publications

(46 citation statements)

References 31 publications

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“…AI technology is actively used in the medical field as well, and its effectiveness has been demonstrated in various results such as medical image analysis and omics analysis [1,[32][33][34][35][36][37][38][39]. In the field of oncology, the introduction of AI is also being actively studied in tumor screening, including lung and breast cancer [40,41]. Practically, as of 2020, more than 60 AI-powered medical devices have been approved by the US FDA (Table 1).…”

Section: The Third Ai Boom and Era Of Deep Learningmentioning

confidence: 99%

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Hamamoto

Suvarna

Yamada³

et al. 2020

Cancers

144

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In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

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Section: The Third Ai Boom and Era Of Deep Learningmentioning

confidence: 99%

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Hamamoto

Suvarna

Yamada³

et al. 2020

Cancers

144

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“…MRI is the most sensitive modality for breast cancer detection and is currently indicated as a supplement to mammography for patients at high risk (59) (67). Breast MRI is still necessary to screen patients at high risk of breast cancer.…”

Section: Applications Of Ai In Breast Mrimentioning

confidence: 99%

Artificial Intelligence in Medical Imaging of the Breast

Lei

Yin

et al. 2021

Front. Oncol.

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Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

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“…Automated computerized detection and diagnostic systems have been introduced as auxiliary means for radiologists for diverse diagnostic processes [9][10][11][12][13][14]. Coronary artery disease is one of the leading causes of life-threatening health problems in developing countries [27,28], but AI is still rarely used for this disease [18].…”

Section: Diagnostic Consistency Of the Six Readersmentioning

confidence: 99%

“…Therefore, for radiologists with less cardiovascular experience, an automated system to aid diagnosis is attractive and promising. Automated arti cial intelligent (AI) algorithms have been applied to the diagnosis of a wide range of disease states [9][10][11][12][13][14]; for example, the use of AI in the detection and diagnosis of breast cancer and colon polyps has improved reader performance, especially for inexperienced or novice readers [10,15,16].…”

mentioning

confidence: 99%

Artificial Intelligent Stenosis Diagnosis in Coronary CTA: Effect on the Performance and Consistency of Readers with Less Cardiovascular Experience

Han

Luo

et al. 2021

Preprint

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Background: To investigate the influence of artificial intelligent (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.Methods: We enrolled 196 patents who had undergone both CCTA and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1 to Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with aid from an AI system, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of six readers were calculated at the patient and vessel levels. Additionally, we evaluated interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader 4, and Reader 5 and Reader 6.Results: The AI system had 94% and 78% sensitivity at the patient and vessel levels, respectively, which were higher than Reader 5 and Reader 6. Reader 3 and Reader 4 aided by AI had a higher sensitivity (range: +7.2%~ +16.6% and +5.9%~ +16.1%, respectively) and NPV (range: +3.7%~ +13.4% and +2.7%~ +4.2%, respectively) than Reader 1 and Reader 2 without AI. There was good interobserver consistency between Reader 3 and Reader 4 in interpreting ≥50% stenosis (Kappa value= 0.75 and 0.80 at the patient and vessel levels, respectively). Only Reader 1 and Reader 2 had poor consistency (Kappa value= 0.25 and 0.37). Reader 5 and Reader 6 had moderate agreement (Kappa value= 0.55 and 0.61).Conclusions: Our study showed that using AI could effectively increase the sensitivity of inexperienced readers and significantly improve consistency in diagnosing coronary stenosis via CCTA.Trial registration: The clinical trial registration number: ChiCTR1900021867Name of registry: Diagnostic performance of artificial intelligence assisted coronary computed tomography angiography for the assessment of coronary atherosclerotic stenosis

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Artificial Intelligence Applied to Breast MRI for Improved Diagnosis

Cited by 91 publications

References 31 publications

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Artificial Intelligence in Medical Imaging of the Breast

Artificial Intelligent Stenosis Diagnosis in Coronary CTA: Effect on the Performance and Consistency of Readers with Less Cardiovascular Experience

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