Comparisons between artificial intelligence computer-aided detection synthesized mammograms and digital mammograms when used alone and in combination with tomosynthesis images in a virtual screening setting

Uematsu, Takayoshi; Nakashima, Kazuaki; Harada, Taiyo L; Nasu, Hatsuko; Igarashi, Tatsuya

doi:10.1007/s11604-022-01327-5

Cited by 12 publications

(2 citation statements)

References 23 publications

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“…The effective utilization of AI by radiologists is expected to not only improve diagnostic performance in the future but also to significantly reduce radiologists' workload and contribute to healthcare cost savings. AI's potential to streamline diagnostic processes and enhance accuracy promises both operational efficiency and reduced strain on healthcare systems, making it a valuable tool in modern medical practice [9,[33][34][35]. GAN is one of the most remarkable methods and has been applied to medical imaging and proven useful in various areas, such as image enhancement, registration, generation, reconstruction, and transformation between images.…”

Section: Discussionmentioning

confidence: 99%

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network

Zama,

Fujioka,

Yamaga

et al. 2023

Medicina

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Background and Objectives: This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images. Materials and Methods: We retrospectively collected approximately 200 breast ultrasound images for each of five representative histological tissue types (cyst, fibroadenoma, scirrhous, solid, and tubule-forming invasive ductal carcinomas) as training images. A deep convolutional GAN (DCGAN) image-generation model synthesized images of the five histological types. Two diagnostic radiologists (reader 1 with 13 years of experience and reader 2 with 7 years of experience) were given a reading test consisting of 50 synthesized and 50 original images (≥1-month interval between sets) to assign the perceived histological tissue type. The percentages of correct diagnoses were calculated, and the reader agreement was assessed using the kappa coefficient. Results: The synthetic and original images were indistinguishable. The correct diagnostic rates from the synthetic images for readers 1 and 2 were 86.0% and 78.0% and from the original images were 88.0% and 78.0%, respectively. The kappa values were 0.625 and 0.650 for the synthetic and original images, respectively. The diagnoses made from the DCGAN synthetic images and original images were similar. Conclusion: The DCGAN-synthesized images closely resemble the original ultrasound images in clinical characteristics, suggesting their potential utility in clinical education and training, particularly for enhancing diagnostic skills in breast ultrasound imaging.

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Section: Discussionmentioning

confidence: 99%

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network

Zama,

Fujioka,

Yamaga

et al. 2023

Medicina

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“…The inception of Deep Learning (DL) has catalyzed a significant progression in artificial intelligence (AI) [1], unlocking numerous possibilities, especially in diagnostic radiology-an arena pivotal for accurate imaging data interpretation. This progression is attributed mainly to the emergence of Convolutional Neural Networks (CNNs) [2,3], which have markedly enhanced image recognition, segmentation, analysis, and improvement of image quality [1,[4][5][6][7][8][9][10][11][12][13][14][15]. This represents a foundational shift in automated feature extraction from imaging data, consequently reducing the time and expertise required for interpreting medical images.…”

Section: Introductionmentioning

confidence: 99%

The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI

Nakaura,

Ito,

Ueda

et al. 2024

Jpn J Radiol

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The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs’ potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

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Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound

et al. 2023

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Purpose This study aimed to evaluate the clinical usefulness of a deep learning-based computer-aided detection (CADe) system for breast ultrasound. Methods The set of 88 training images was expanded to 14,000 positive images and 50,000 negative images. The CADe system was trained to detect lesions in real- time using deep learning with an improved model of YOLOv3-tiny. Eighteen readers evaluated 52 test image sets with and without CADe. Jackknife alternative free-response receiver operating characteristic analysis was used to estimate the effectiveness of this system in improving lesion detection. Result The area under the curve (AUC) for image sets was 0.7726 with CADe and 0.6304 without CADe, with a 0.1422 difference, indicating that with CADe was significantly higher than that without CADe (p < 0.0001). The sensitivity per case was higher with CADe (95.4%) than without CADe (83.7%). The specificity of suspected breast cancer cases with CADe (86.6%) was higher than that without CADe (65.7%). The number of false positives per case (FPC) was lower with CADe (0.22) than without CADe (0.43). Conclusion The use of a deep learning-based CADe system for breast ultrasound by readers significantly improved their reading ability. This system is expected to contribute to highly accurate breast cancer screening and diagnosis.

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Comparisons between artificial intelligence computer-aided detection synthesized mammograms and digital mammograms when used alone and in combination with tomosynthesis images in a virtual screening setting

Cited by 12 publications

References 23 publications

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network

The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI

Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound

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