New research demonstrates that high BMI negatively impacts both human and AI performance in chest low-dose CT interpretation, highlighting dataset diversity concerns.
Key Details
- 1Higher BMI leads to increased image noise and reduced CT scan quality, especially in low-dose protocols.
- 2Many AI models lack diverse training data reflecting a broad range of BMIs.
- 3Study compared top 1.5% highest and lowest BMI patients using chest LDCT scans from the Lifelines cohort.
- 4AI and human readers both showed impacted nodule detection sensitivity and false positive rates at extreme BMIs.
- 5Evaluation was performed by two expert chest radiologists.
Why It Matters
This study exposes a critical blind spot in radiology AI—model performance can deteriorate for patients at BMI extremes due to suboptimal imaging and lack of representativeness in training datasets. Ensuring AI equity and reliability in clinical practice requires addressing these biases.
Source
Health Imaging
Related News
•Health Imaging
Most FDA-Cleared AI Devices Lack Pre-Approval Safety Data, Study Finds
A new study finds fewer than 30% of FDA-cleared AI medical devices reported key safety or adverse event data before approval.
•Radiology Business
Cigna Expands Nationwide Coverage for CT Imaging AI Tools
Cigna will reimburse for CT-based plaque analysis AI software for millions of members nationwide.
•AuntMinnie
AI for Breast Cancer Screening Not Cost-Effective, Study Finds
AI-assisted breast cancer screening showed minor clinical benefits over DBT alone but was not cost-effective at standard willingness-to-pay thresholds.