Artificial Intelligence

MGH’s Multimodal Thyroid Ultrasound AI

AI Platform

An MGH and Harvard Medical team developed a multimodal ultrasound AI platform that applies an interesting mix of AI techniques to accurately detect and stage thyroid cancer, potentially improving diagnosis and treatment planning.

The Platform – The platform combines radiomics, topological data analysis (TDA), ML-based TI-RADS assessments, and deep learning, allowing them to capture more data, minimize noise, and improve prediction accuracy.

The Study – Starting with 1,346 ultrasound images from 784 patients, the researchers trained the multimodal AI platform with 362 nodules (103 malignant) and validated it against a pair of internal (51 malignant, 98 benign) and external (270 malignant, 50 benign) datasets, finding that:

  • The platform predicted 98.7% of internal dataset malignancies (0.99 AUC)
  • The platform predicted 91.4% of external dataset malignancies (0.94 AUC)
  • The individual AI methods were far less accurate (80% to 89% w/ internal)
  • A version of the platform accurately predicted nodal pathological stages (93% for T-stage, 89% for N-stage, 98% for extrathyroidal extension)
  • The platform predicted BRAF mutations with 96% accuracy

Next Steps – The researchers plan to validate their multimodal platform in prospective multicenter clinical trials, including in low-resource countries where it might be particularly helpful.


The Takeaway

We cover plenty of ultrasound AI and thyroid cancer imaging studies, but this team’s multi-AI approach is unique and appears promising. A multimodal AI platform like this might make thyroid cancer diagnosis more efficient and less subjective, avoid unnecessary biopsies, allow non-invasive staging and mutation assessment, and lead to more personalized treatments. That would be a major accomplishment, and might suggest that similar multimodal AI platforms could be developed for other cancers and imaging modalities.

Get every issue of The Imaging Wire, delivered right to your inbox.

You're signed up!

It's great to have you as a reader. Check your inbox for a welcome email.

-- The Imaging Wire team