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
•AuntMinnie
AI Models Reveal Racial Disparities in Breast Cancer Patterns
Machine learning models reveal significant racial disparities and key predictors in breast cancer incidence across diverse groups.
•AuntMinnie
AI Algorithm Streamlines and Standardizes Shoulder Ultrasound Acquisition
A multitask AI system demonstrated high accuracy in standardizing and guiding shoulder musculoskeletal ultrasound imaging.
•Radiology Business
Deepfake X-rays Fool Radiologists and AI, Raising Security Concerns
Both radiologists and AI models struggle to differentiate between authentic and AI-generated ('deepfake') radiographic images, raising major security and clinical concerns.