A deep-learning model to assist thyroid nodule diagnosis and management

Zhang, Bin; Jin, Zhe; Zhang, Shuixing

doi:10.1016/s2589-7500(21)00108-4

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…As such systems are less affected by inter-observer variations, they are well received by sonographers. Currently, some studies have been conducted on thyroid nodule classification using AI (15)(16)(17). Commercial thyroid computer-aided diagnosis (CAD) systems have been integrated into US machines for real-time diagnosis, demonstrating an Acc similar to that of an experienced radiologist (18).…”

Section: Discussionmentioning

confidence: 99%

Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients

Zhao,

Yin,

Liu

et al. 2024

Front. Oncol.

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ObjectiveThis study aimed to develop a deep learning system to identify and differentiate the metastatic cervical lymph nodes (CLNs) of thyroid cancer.MethodsFrom January 2014 to December 2020, 3059 consecutive patients with suspected with metastatic CLNs of thyroid cancer were retrospectively enrolled in this study. All CLNs were confirmed by fine needle aspiration. The patients were randomly divided into the training (1228 benign and 1284 metastatic CLNs) and test (307 benign and 240 metastatic CLNs) groups. Grayscale ultrasonic images were used to develop and test the performance of the Y-Net deep learning model. We used the Y-Net network model to segment and differentiate the lymph nodes. The Dice coefficient was used to evaluate the segmentation efficiency. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the classification efficiency.ResultsIn the test set, the median Dice coefficient was 0.832. The sensitivity, specificity, accuracy, PPV, and NPV were 57.25%, 87.08%, 72.03%, 81.87%, and 66.67%, respectively. We also used the Y-Net classified branch to evaluate the classification efficiency of the LNs ultrasonic images. The classification branch model had sensitivity, specificity, accuracy, PPV, and NPV of 84.78%, 80.23%, 82.45%, 79.35%, and 85.61%, respectively. For the original ultrasonic reports, the sensitivity, specificity, accuracy, PPV, and NPV were 95.14%, 34.3%, 64.66%, 59.02%, 87.71%, respectively. The Y-Net model yielded better accuracy than the original ultrasonic reports.ConclusionThe Y-Net model can be useful in assisting sonographers to improve the accuracy of the classification of ultrasound images of metastatic CLNs.

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Section: Discussionmentioning

confidence: 99%

Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients

Zhao,

Yin,

Liu

et al. 2024

Front. Oncol.

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“…Recently, artificial intelligence (AI) has shown great potential in assisting doctors in various medical fields with their diagnoses [17][18][19]. Particularly, deep learning techniques based on deep convolutional neural networks (DCNN) have demonstrated extraordinary capabilities for medical image classification, detection, and segmentation [20,21]. Benefiting from its super-resolution performance on microscopic images, AI can automatically infer complex microscopic imaging structures (i.e., abnormalities in the extent and colour intensity of mucosal tubular branches) and identify quantitative pixel-level features [22], which are usually indistinguishable from the human eye.…”

Section: Introductionmentioning

confidence: 99%

Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data

Li,

Gu,

Yue

et al. 2023

J Transl Med

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Background Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists. Methods All 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance of the LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS. Results LPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965–0·987. In the laryngologist-machine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications. Conclusions LPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.

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“…The use of these methods has been proven to be an efficient way to overcome the shortcomings of traditional image analysis on many sub-specialty applications. DL methods in medical image analysis have been applied in MRI tumor grading [8][9][10], thyroid nodule ultrasound classification [11][12][13] and CT pulmonary nodule detection [14][15][16]. However, only a limited number of studies have been performed to analyze the musculoskeletal imaging associated with the lesion.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Osteochondral Lesions of the Talus via Deep Learning

Wang

Li²,

Zhu

et al. 2022

Front. Phys.

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Screening of osteochondral lesions of the talus (OLTs) from MR imags usually requires time and efforts, and in most case lesions with small size are often missed in clinical practice. Thereore, it puts forward higher requirements for a more efficient OLTs screening method. To develop an automatic screening system for osteochondral lesions of the talus (OLTs), we collected 92 MRI images of patients with ankle pain from Qilu Hospital of Shandong University and proposed an AI (artificial intelligence) aided lesion screening system, which is automatic and based on deep learning method. A two-stage detection method based on the cascade R-CNN model was proposed to significantly improve the detection performance by taking advantage of multiple intersection-over-union thresholds. The backbone network was based on ResNet50, which was a state-of-art convolutional neural network model in image classification task. Multiple regression using cascaded detection heads was applied to further improve the detection precision. The mean average precision (mAP) that is adopted as major metrics in the paper and mean average recall (mAR) was selected to evaluate the performance of the model. Our proposed method has an average precision of 0.950, 0.975, and 0.550 for detecting the talus, gaps and lesions, respectively, and the mAP, mAR was 0.825, 0.930. Visualization of our network performance demonstrated the effectiveness of the model, which implied that accurate detection performance on these tasks could be further used in real clinical practice.

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A deep-learning model to assist thyroid nodule diagnosis and management

Cited by 5 publications

References 3 publications

Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients

Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients

Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data

Automatic Detection of Osteochondral Lesions of the Talus via Deep Learning

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