Evidence
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
Curr Probl Diagn Radiol. 2024 Jul 9:S0363-0188(24)00110-5. doi: 10.1067/j.cpradiol.2024.07.006. Online ahead of print.
ABSTRACT
PURPOSE: To validate the performance of a recently created risk stratification system (RSS) for thyroid nodules on ultrasound, the Artificial Intelligence Thyroid Imaging Reporting and Data System (AI TI-RADS).
MATERIALS AND METHODS: 378 thyroid nodules from 320 patients were included in this retrospective evaluation. All nodules had ultrasound images and had undergone fine needle aspiration (FNA). 147 nodules were Bethesda V or VI (suspicious or diagnostic for malignancy), and 231 were Bethesda II (benign). Three radiologists assigned features according to the AI TI-RADS lexicon (same categories and features as the American College of Radiology TI-RADS) to each nodule based on ultrasound images. FNA recommendations using AI TI-RADS and ACR TI-RADS were then compared and sensitivity and specificity for each RSS were calculated.
RESULTS: Across three readers, mean sensitivity of AI TI-RADS was lower than ACR TI-RADS (0.69 vs 0.72, p < 0.02), while mean specificity was higher (0.40 vs 0.37, p < 0.02). Overall total number of points assigned by all three readers decreased slightly when using AI TI-RADS (5,998 for AI TI-RADS vs 6,015 for ACR TI-RADS), including more values of 0 to several features.
CONCLUSION: AI TI-RADS performed similarly to ACR TI-RADS while eliminating point assignments for many features, allowing for simplification of future TI-RADS versions.
PMID:39033064 | DOI:10.1067/j.cpradiol.2024.07.006
![Google](https://www.google.com/images/branding/googlelogo/2x/googlelogo_light_color_92x30dp.png)
![Google Keep](https://www.gstatic.com/images/branding/product/1x/keep_48dp.png)
![Share on Linkedin](https://psychiatryai.com/wp-content/uploads/2023/10/linkedin-logo-png-2048-1.png)
Estimated reading time: 4 minute(s)
Latest: Psychiatryai.com #RAISR4D Evidence
![](/wp-content/uploads/2024/04/bd462cc11bcf0bd0d0d6f1d0f8b7cd04-modified-1.png)
Cool Evidence: Engaging Young People and Students in Real-World Evidence
![](/wp-content/uploads/2024/04/bd462cc11bcf0bd0d0d6f1d0f8b7cd04-modified-1.png)
Real-Time Evidence Search [Psychiatry]
![](/wp-content/uploads/2024/04/pubmed.png)
AI Research
![](/wp-content/uploads/2024/05/Il5nR_nf_400x400-modified-1.png)
Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
🌐 90 Days
Evidence Blueprint
Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
☊ AI-Driven Related Evidence Nodes
(recent articles with at least 5 words in title)
More Evidence
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)