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Machine learning and thyroid and ultrasound

Prabal Poudel, Alfredo Illanes, Debdoot Sheet, Michael Friebe
The thyroid is one of the largest endocrine glands in the human body, which is involved in several body mechanisms like controlling protein synthesis and the body's sensitivity to other hormones and use of energy sources. Hence, it is of prime importance to track the shape and size of thyroid over time in order to evaluate its state. Thyroid segmentation and volume computation are important tools that can be used for thyroid state tracking assessment. Most of the proposed approaches are not automatic and require long time to correctly segment the thyroid...
2018: Journal of Healthcare Engineering
Wenfeng Song, Shuai Li, Ji Liu, Hong Qin, Bo Zhang, Zhang Shuyang, Aimin Hao
Thyroid ultrasonography is a widely-used clinical technique for nodule diagnosis in thyroid regions. However, it remains difficult to detect and recognize the nodules due to low contrast, high noise, and diverse appearance of nodules. In today's clinical practice, senior doctors could pinpoint nodules by analyzing global context features, local geometry structure, and intensity changes, which would require rich clinical experience accumulated from hundreds and thousands of nodule case studies. To alleviate doctors' tremendous labor in the diagnosis procedure, we advocate a machine learning approach to the detection and recognition tasks in this paper...
July 3, 2018: IEEE Journal of Biomedical and Health Informatics
Jianfu Xia, Huiling Chen, Qiang Li, Minda Zhou, Limin Chen, Zhennao Cai, Yang Fang, Hong Zhou
BACKGROUND AND OBJECTIVES: It is important to be able to accurately distinguish between benign and malignant thyroid nodules in order to make appropriate clinical decisions. The purpose of this study was to improve the effectiveness and efficiency for discriminating the malignant from benign thyroid cancers based on the Ultrasonography (US) features. METHODS: There were 114 benign nodules in 106 patients (82 women and 24 men) and 89 malignant nodules in 81 patients (69 women and 12 men) included in this study...
August 2017: Computer Methods and Programs in Biomedicine
Hongxun Wu, Zhaohong Deng, Bingjie Zhang, Qianyun Liu, Junyong Chen
OBJECTIVE: The purpose of this article is to construct classifier models using machine learning algorithms and to evaluate their diagnostic performances for differentiating malignant from benign thyroid nodules. MATERIALS AND METHODS: This study included 970 histopathologically proven thyroid nodules in 970 patients. Two radiologists retrospectively reviewed ultrasound images, and nodules were graded according to a five-tier sonographic scoring system. Statistically significant variables based on an experienced radiologist's observations were obtained with attribute optimization using fivefold cross-validation and applied as the input nodes to build models for predicting malignancy of nodules...
October 2016: AJR. American Journal of Roentgenology
Maoxin Wu
Our previous study showed that the sensitivity of head and neck fine-needle aspiration (FNA) procedures was significantly better in the cytopathologist-performed group than in the noncytopathologist-performed group (96 versus 67%). Recently, cytopathologists have learned to use ultrasound machines to assist them in performing FNA procedures. This study was designed to assess whether cytopathologist-performed FNAs with ultrasound guidance can improve diagnostic value in comparison to those done without ultrasound guidance...
October 2011: Diagnostic Cytopathology
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