The Transverse Ultrasonogram of Thyroid Papillary Carcinoma Has a Better Prediction Accuracy Than the Longitudinal One

Yu, Bin; Wang, Zhongyu; Zhu, Renxiang; Feng, Xin; Qi, Mingran; Li, Jialiang; Zhao, Ruxing; Huang, Lan; Xin, Ruihao; Li, Fan; Zhou, Fengfeng

doi:10.1109/access.2019.2926377

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…In [215], texture information was used to diagnose TN malignancy through a two-level 2D wavelet transform. Other works exploring this area can be found in [216,217].…”

Section: Fe2 Texture Descriptionmentioning

confidence: 99%

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AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Habchi,

Himeur,

Kheddar

et al. 2023

Systems

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Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years, offering advanced tools and methodologies that promise to revolutionize patient outcomes. This review provides an exhaustive overview of the contemporary frameworks employed in the field, focusing on the objective of AI-driven analysis and dissecting methodologies across supervised, unsupervised, and ensemble learning. Specifically, we delve into techniques such as deep learning, artificial neural networks, traditional classification, and probabilistic models (PMs) under supervised learning. With its prowess in clustering and dimensionality reduction, unsupervised learning (USL) is explored alongside ensemble methods, including bagging and potent boosting algorithms. The thyroid cancer datasets (TCDs) are integral to our discussion, shedding light on vital features and elucidating feature selection and extraction techniques critical for AI-driven diagnostic systems. We lay out the standard assessment criteria across classification, regression, statistical, computer vision, and ranking metrics, punctuating the discourse with a real-world example of thyroid cancer detection using AI. Additionally, this study culminates in a critical analysis, elucidating current limitations and delineating the path forward by highlighting open challenges and prospective research avenues. Through this comprehensive exploration, we aim to offer readers a panoramic view of AI’s transformative role in thyroid cancer diagnosis, underscoring its potential and pointing toward an optimistic future.

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“…In [215], texture information was used to diagnose TN malignancy through a two-level 2D wavelet transform. Other works exploring this area can be found in [216,217].…”

Section: Fe2 Texture Descriptionmentioning

confidence: 99%

“…The LBP are features employed in computer vision to recognize textures or objects in digital images. LBP have been utilized to detect thyroid cancer in [216]. The combination of LBP and DL has also been proposed to classify benign and malignant thyroid nodules in [221,222].…”

Section: Fe4 Local Binary Patterns (Lbp)mentioning

confidence: 99%

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Habchi,

Himeur,

Kheddar

et al. 2023

Systems

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“…There are FDA cleared AI software to diagnose diabetic retinopathy, stroke, and breast lesions (12)(13)(14)(15). AI algorithms have been used to classify thyroid nodules objectively (16)(17)(18)(19). Given a thyroid ultrasound image, these algorithms can predict whether a thyroid nodule is benign or malignant.…”

Section: Similar Problems Exist In Other Medical Domains and Artificimentioning

confidence: 99%

AIBx, artificial intelligence model to risk stratify thyroid nodules

Thomas

Haertling

2021

Preprint

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Background Current classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivity in medical image interpretation. 1 out of 2 women over the age of 50 may have a thyroid nodule and at present the only way to exclude malignancy is through invasive procedures. Hence, there exists a need for noninvasive objective classification of thyroid nodules. Some cancers have benign appearance on ultrasonogram. Hence, we decided to create an image similarity algorithm rather than image classification algorithm. Methods Ultrasound images of thyroid nodules from patients who underwent either biopsy or thyroid surgery from February of 2012 through February of 2017 in our institution were used to create AI models. Nodules were excluded if there was no definitive diagnosis of benignity or malignancy. 482 nodules met the inclusion criteria and all available images from these nodules were used to create the AI models. Later, these AI models were used to test 103 thyroid nodules which underwent biopsy or surgery from March of 2017 through July of 2018. Results Negative predictive value of the image similarity model was 93.2%. Sensitivity, specificity, positive predictive value and accuracy of the model was 87.8%, 78.5%, 65.9% and 81.5% respectively. Conclusion When compared to published results of ACR TIRADS and ATA classification system, our image similarity model had comparable negative predictive value with better sensitivity specificity and positive predictive value. By using image similarity AI models, we can eliminate subjectivity and decrease the number of unnecessary biopsies. Using image similarity AI model, we were able to create an explainable AI model which increases physician′s confidence in the predictions.

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