Machine learning approaches for pathologic diagnosis

Komura, Daisuke; Ishikawa, Shumpei

doi:10.1007/s00428-019-02594-w

Cited by 112 publications

(80 citation statements)

References 34 publications

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“…In detail, machine learning algorithms use computed tomography (CT), magnetic resonance imaging, ultrasound, pathology image, fundus image, and endoscope data to diagnose or classify the severity of the disease. 8 16 17 18 19 20 21 22 When the machine learning algorithm was applied to the real-time colonoscopy, the accuracy of the diagnosis was 94% and the negative predictive value was 96% in analyzing 466 tiny polyps. 23 24 Among the many deep learning algorithms, the convolutional neural network algorithm, which retains high performance in image pattern analysis, has proven to be beneficial in analyzing medical images with complex patterns.…”

Section: Ai Application Areas In Health Carementioning

confidence: 99%

Artificial Intelligence in Health Care: Current Applications and Issues

et al. 2020

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In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

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Section: Ai Application Areas In Health Carementioning

confidence: 99%

Artificial Intelligence in Health Care: Current Applications and Issues

et al. 2020

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“…The major obstacle in using ML in pathological imaging is inadequate image annotations. At present, there exist many technologies to address this concern [3]. For example, generative adversarial networks are used for pathological data analysis to automatically prepare image datasets necessary for subsequent deep learning.…”

Section: How Is ML Applied To Develop Pre-cision Medicine?mentioning

confidence: 99%

Utilizing machine learning to discern hidden clinical values from big data in urology

et al. 2020

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“…A lack of access to dermatopathology expertise in this context can slow diagnostic turnaround times, resulting in delays in patient care and leading to potential adverse impacts on clinical outcomes. In this scenario, the computer-aided diagnosis (CAD) system reduces intraobserver and interobserver variability and improves the accuracy of pathology interpretation (5,6).…”

Section: Introductionmentioning

confidence: 99%