Stratification of thyroid nodules by Eu-TIRADS categories using transfer learning of convolutional neural networks

Fartushnyi, Eduard N.; Sych, Yulia P.; Fartushnyi, I. E.; Koshechkin, Konstantin; Lebedev, Georgy

doi:10.14341/ket12724

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…The use of LSTM cells allows an RNN to store and use information about previous states for a long time, which makes it especially effective for processing sequential data with longterm dependencies [11]. This is especially important when thyroid tests are collected several times over a certain period.…”

Section: Recurrent Approachesmentioning

confidence: 99%

Using Deep Learning to Generate and Classify Thyroid Cytopathology Reports According to The Bethesda System

Diuldin,

Makanov,

Shifman

et al. 2024

RIA

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The purpose of this paper is to study approaches to the intellectual processing of Russianlanguage textual medical information concerning the thyroid cytopathology description to solve the issues of their classification and generation of the text of the medical report, as well as augmentation of descriptions in their acute shortage. Over the past decade, the field of biomedicine has not undergone significant changes. Approaches to analyzing patients' problems are mostly based on manual processing and expert knowledge of doctors. The paper considers the creation of a machine-learning pipeline containing a full cycle of data preprocessing and model training in the field of thyroid nodules fine-needle aspiration classification according to the Bethesda thyroid cytopathology reporting system. Sequential and transformer neural networks were used to design the architecture of deep learning models. The paper proposes approaches for cleaning and preprocessing raw medical descriptions to the required type. The obtained results show that sequential neural networks have greater accuracy on small data sets, and transformation architectures are superior to others when generating cytopathological reports on large amounts of data. The solution obtained in the study can be used as an additional reference tool for thyroid cytologists.

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Section: Recurrent Approachesmentioning

confidence: 99%

Using Deep Learning to Generate and Classify Thyroid Cytopathology Reports According to The Bethesda System

Diuldin,

Makanov,

Shifman

et al. 2024

RIA

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Application of artificial intelligence in ultrasound diagnostics of thyroid nodules

Troshina,

Zakharova,

Tsyguleva

et al. 2024

Clin Exp Thyroid

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BACKGROUND: the use of artificial intelligence in ultrasound diagnosis of thyroid nodules is expected and quite promising. However, in order to understand this, it is necessary to see how a doctor works with its help, diagnosing diseases step by step, and how exactly this intelligence is implemented in practical healthcare. The current publication provides an overview of existing intelligent systems for supporting medical decisions in thyroidology, and describes in detail the capabilities of the Russian intelligent computer assistant for ultrasound diagnostics - a system for stratifying thyroid nodules by EU-TIRADS categories.AIM: increasing the accuracy and reducing the time of ultrasound diagnostics in the study of thyroid nodules through the use of an intelligent system for assisting the ultrasound doctor at various stages of his activity with demonstration of the actions of the “assistant”.MATERIALS AND METHODS: to understand the possibilities of ultrasound doctors using artificial intelligence in their work, the proposed solution is divided into stages, each of which demonstrates the additional capabilities that a doctor has when using intelligent computer vision methods. Various artificial neural network architectures are used as an intellectual base, which can be further trained like a human on new medical data.RESULTS: the proposed intelligent solution allows the ultrasound doctor to have a “second opinion” at his workplace, which, by processing ultrasound cine loops, allows him to solve the problems of segmentation and stratification of thyroid nodules according to EU-TIRADS categories with an accuracy of 70%, i.e. at the level of a doctor with 5 years of experience. The proposed data driven approach will improve its accuracy as new patient loops are processed.CONCLUSION: the narrative leads the reader to understand in what diagnostic processes it is useful to use artificial intelligence methods in the ultrasound diagnosis of thyroid nodules, and how natural and artificial intelligence can effectively interact within the framework of a software web application.

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Regulation of artificial intelligence in medicine

Koshechkin

2023

PoMPh

Self Cite

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A study on the regulation of artificial intelligence (AI) in healthcare, includes a brief overview of the current state of use of AI in healthcare and its potential benefits and risks. The article summarizes the current regulations that exist for AI in healthcare, including any relevant laws, guidelines, and best practices, including information on regulatory bodies such as the FDA and HIPAA. The ethical considerations arising from the use of AI in healthcare, such as patient confidentiality and data security, bias in algorithms, and transparency in decision making, are given. Examples of AI in healthcare are given that illustrate the challenges and opportunities provided by the technology, including both successful and unsuccessful implementations. Future developments in AI and healthcare are described, including emerging technologies and trends, and predictions of how rules might evolve in response to these developments. Summarize and provide recommendations for addressing regulatory challenges related to AI in healthcare.

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Stratification of thyroid nodules by Eu-TIRADS categories using transfer learning of convolutional neural networks

Cited by 3 publications

References 5 publications

Using Deep Learning to Generate and Classify Thyroid Cytopathology Reports According to The Bethesda System

Using Deep Learning to Generate and Classify Thyroid Cytopathology Reports According to The Bethesda System

Application of artificial intelligence in ultrasound diagnostics of thyroid nodules

Regulation of artificial intelligence in medicine

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