ArticleMedical science monitor : international medical journal of experimental and clinical research2020
Visual Interpretability in Computer-Assisted Diagnosis of Thyroid Nodules Using Ultrasound Images.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 29 citations in OpenAlex.
- Review
- Spatial and temporal trend analysis of the burden of endocrine-related cancers among women of reproductive age in the Asia-Pacific region from 1990 to 2021: results based on the GBD study.Frontiers in oncology · 2025Article
- Clinical Application of Artificial Intelligence in the Ultrasound Classification of Hepatic Cystic Echinococcosis.The American journal of tropical medicine and hygiene · 2024Article
- Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population.Nature communications · 2024Article
- Diagnostic value of a computer-assisted diagnosis system for the ultrasound features in thyroid nodules.Archives of endocrinology and metabolism · 2023Article
- Study on diagnosis of thyroid nodules based on convolutional neural network.Radiologie (Heidelberg, Germany) · 2023Article
- Aided diagnosis of thyroid nodules based on an all-optical diffraction neural network.Quantitative imaging in medicine and surgery · 2023Article
- Explainability of deep learning models in medical video analysis: a survey.PeerJ. Computer science · 2023Article
- Artificial intelligence technology enhances the performance of shear wave elastography in thyroid nodule diagnosis.American journal of translational research · 2023Article
- Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography-A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Applications of Explainable Artificial Intelligence in Diagnosis and Surgery.Diagnostics (Basel, Switzerland) · 2022Review
- Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis.International journal of endocrinology · 2022Article
- Reliability of a computer-aided system in the evaluation of indeterminate ultrasound images of thyroid nodules.European thyroid journal · 2022Article
- The value of color Doppler ultrasound in the diagnosis of thyroid nodules: a systematic review and meta-analysis.Gland surgery · 2021Article
- Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives.Frontiers in oncology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND The number of studies on deep learning in artificial intelligence (AI)-assisted diagnosis of thyroid nodules is increasing. However, it is difficult to explain what the models actually learn in artificial intelligence-assisted medical research. Our aim is to investigate the visual interpretability of the computer-assisted diagnosis of malignant and benign thyroid nodules using ultrasound images. MATERIAL AND METHODS We designed and implemented 2 experiments to test whether our proposed model learned to interpret the ultrasound features used by ultrasound experts to diagnose thyroid nodules. First, in an anteroposterior/transverse (A/T) ratio experiment, multiple models were trained by changing the A/T ratio of the original nodules, and their classification, accuracy, sensitivity, and specificity were tested. Second, in a visualization experiment, class activation mapping used global average pooling and a fully connected layer to visualize the neural network to show the most important features. We also examined the importance of data preprocessing. RESULTS The A/T ratio experiment showed that after changing the A/T ratio of the nodules, the accuracy of the neural network model was reduced by 9.24-30.45%, indicating that our neural network model learned the A/T ratio information of the nodules. The visual experiment results showed that the nodule margins had a strong influence on the prediction of the neural network. CONCLUSIONS This study was an active exploration of interpretability in the deep learning classification of thyroid nodules. It demonstrated the neural network-visualized model focused on irregular nodule margins and the A/T ratio to classify thyroid nodules.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.