Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer

Wang, Jeff; Kato, Fumiki; Yamashita, Hiroko; Baba, Motoi; Cui, Yi; Li, Ruijiang; Oyama‐Manabe, Noriko; Shirato, Hiroki

doi:10.1007/s10278-016-9922-9

Cited by 16 publications

(10 citation statements)

References 57 publications

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“…There is a great deal of overlap between detection and classification with CNNs in mammography as many CNNs designed for detection also ultimately aim to classify lesions. Additional applications in breast imaging include accurate classification of breast density on mammograms (38) and classification of tumors (9).…”

Section: Classificationmentioning

confidence: 99%

Deep Learning in Radiology

et al. 2018

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As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably powerful tool for image processing in recent years. In this work, the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning provides an overview of DL for the radiologist. This article aims to present an overview of DL in a manner that is understandable to radiologists; to examine past, present, and future applications; as well as to evaluate how radiologists may benefit from this remarkable new tool. We describe several areas within radiology in which DL techniques are having the most significant impact: lesion or disease detection, classification, quantification, and segmentation. The legal and ethical hurdles to implementation are also discussed. By taking advantage of this powerful tool, radiologists can become increasingly more accurate in their interpretations with fewer errors and spend more time to focus on patient care.

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Section: Classificationmentioning

confidence: 99%

Deep Learning in Radiology

et al. 2018

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“…It is reported that AI has been used in medical work such as disease diagnosis and screening (45)(46)(47)(48)(49) and is used to predict the occurrence of perioperative adverse events by establishing large databases and machine deep learning (50,51). In the prevention and control of COVID-19, AI has shown even more great advantages and value in adjunctive diagnosis, image analysis, drug development, temperature detection, robotic disinfection, pandemic modeling, and health management (52)(53)(54).…”

Section: Ai Technologymentioning

confidence: 99%

Advances in studies on imaging and artificial intelligence technology-assisted difficult airway assessment

Wang¹,

Xia²,

Jiang³

2021

Front Oral Maxillofac Med

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Endotracheal intubation is an indispensable part to ensure oxygen supply for patients who undergo general anesthesia in surgeries. However, despite great progress and improvement in intubation technology and equipment, the incidence of perioperative complications and disabilities caused by difficult airway has not been well improved, especially for unpredictable difficult airway. At present, the accuracy of the difficult airway assessment methods are not high and the process is complex, so it is our focus to optimize the difficult airway prediction process and improve the prediction accuracy. At present, imaging and artificial intelligence (AI) face recognition technology as an aid to the assessment of difficult airways is significantly better than the traditional assessment methods. However, imaging techniques such as X-ray, CT, MRI, and ultrasound have their own clinical application value, while at the same time have many limitations, such as radiation, medical costs, equipment requirements, and medical staff burden, which prevent them from being widely used in clinical practice. On the other hand, AI technology in difficult airway identification is still in the sprouting stage, with the technology being immature and lacks sufficient research evidence to support it. For this reason, we have reviewed the literature on imaging technology and AI technology-assisted assessment of difficult airway in recent years, and we hope to provide new ideas for further research and to shed light on the emergence of a convenient and accurate method for difficult airway early warning, thus optimizing the clinical workflow, reducing the risk of misclassification, and ensuring the perioperative life safety of patients.

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“…It gives a broader range of resolution and densities in dense breasts with minimal noise during image accession [15]. It does not require additional images or exposure of patients to unnecessary radiation and uncomfortable tests for the processing of breast images [16]. DM has given a platform to the CAD software that is used to produce data in digital formats with improved sensitivity in the dense breasts in pre-or postmenopausal females or females under the age of 50 years [17].…”

Section: Digital Mammographymentioning

confidence: 99%

Advanced and futuristic approaches for breast cancer diagnosis

et al. 2020

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Background Breast cancer is the most frequent cancer and one of the most common causes of death in women, impacting almost 2 million women each year. Tenacity or perseverance of breast cancer in women is very high these days with an extensive increasing rate of 3 to 5% every year. Along with hurdles faced during treatment of breast tumor, one of the crucial causes of delay in treatment is invasive and poor diagnostic techniques for breast cancer hence the early diagnosis of breast tumors will help us to improve its management and treatment in the initial stage. Main body Present review aims to explore diagnostic techniques for breast cancer that are currently being used, recent advancements that aids in prior detection and evaluation and are extensively focused on techniques that are going to be future of breast cancer detection with better efficiency and lesser pain to patients so that it helps to a physician to prevent delay in treatment of cancer. Here, we have discussed mammography and its advanced forms that are the need of current era, techniques involving radiation such as radionuclide methods, the potential of nanotechnology by using nanoparticle in breast cancer, and how the new inventions such as breath biopsy, and X-ray diffraction of hair can simply use as a prominent method in breast cancer early and easy detection tool. Conclusion It is observed significantly that advancement in detection techniques is helping in early diagnosis of breast cancer; however, we have to also focus on techniques that will improve the future of cancer diagnosis in like optical imaging and HER2 testing.

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Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer

Cited by 16 publications

References 57 publications

Deep Learning in Radiology

Deep Learning in Radiology

Advances in studies on imaging and artificial intelligence technology-assisted difficult airway assessment

Advanced and futuristic approaches for breast cancer diagnosis

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