- Sep 15
- 3 min read
AI and Thyroid Health: Game Changer New Technology for Endocrinologist
Interpretation of thyroid nodules is time-consuming and resource-intensive in ultrasonography. Despite the ACR® Thyroid Imaging Reporting and Data System (TI-RADS™), clinicians often disagree on categorization, leading to unnecessary biopsies and overtreatment with negligible clinical outcomes. How can clinicians reduce variability in TI-RADS categorization and enhance decision-making confidence and speed?
Thyroid Assistant, powered by Koios DS™, is a clinical decision support software that automatically populates all TI-RADS descriptors and provides an AI cancer risk assessment. Decision support is provided to improve characterization consistency and accuracy across your department, and the results are available in seconds.
Using Thyroid Assistant, clinicians simply click on a button to generate a TI-RADS grading and assessment of a lesion's risk of malignancy. The software additionally provides a suggestion on whether the patient has benign nodules or should be referred for Fine Needle Aspiration (FNA) or further monitoring.
It is simple to grasp. The clinician measures and segments the lesion using a one-touch Auto Contour. A clinician selects the Koios button. Lesion points are automatically assigned by the software using five feature categories: Composition, Echogenicity, Shape, Margin, and Echogenic Foci. The TI-RADS score is calculated by summing the points and assigning the value to one of five categories: Benign, Not Suspicious, Mildly Suspicious, Moderately Suspicious, and Highly Suspicious.
Thyroid Assistant uses deep learning based on the optional Koios AI Adapter to derive a cancer risk assessment from TI-RADS and improve diagnostic performance. The lesion size and that level of invasion provide the basis for FNA versus follow-up recommendations, resulting in improved detection and consistency and, perhaps, fewer unnecessary biopsies.
Using AI and machine learning, Thyroid Assistant from Koios DS recognizes patterns in thyroid tissue, based on over 350,000 images. In scientific studies, the tool helped users at all levels of expertise make more informed FNAs compared to their individual interpretations: The department experienced 41% less variability among readers, which resulted in more consistent classifications across readers. Plus, interpretation time fell by 24% compared to non-Koios-aided exams – enhancing the patient experience as well as department productivity.
Artificial intelligence is becoming a useful tool to assist in the diagnosis and risk classification of thyroid diseases. Clinical laboratories may improve their performance using artificial intelligence companions, which would also provide support in clinical decision making. Artificial intelligence may offer in vitro diagnostic companies novel options for the design and improvement of TFT in the context of network medicine. AI has the potential to enhance care pathways, laboratory testing prescriptions, clinical diagnoses, and patient outcomes in addition to being incorporated into clinical pathways. It may also aid in the detection of molecular mechanisms and potential new treatment strategies in difficult thyroid disorders.
Diagnosis and risk classification in thyroid conditions are becoming aided by artificial intelligence. Clinical laboratories can benefit significantly from artificial intelligence companions for process optimization as well as clinical decision support. In vitro diagnostic companies can benefit from artificial intelligence tools as they develop and improve TFTs. AI can be used to enhance care pathways, laboratory test prescriptions, clinical diagnoses, and patient outcomes in clinical pathways. AI will assist in better understanding difficult thyroid conditions, detecting molecular mechanisms, and identifying new treatment strategies in network medicine.
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