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CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.

Marcus A Badgeley^1,2,3, Manway Liu³, Benjamin S Glicksberg⁴

¹Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Bioinformatics (Oxford, England)

|October 11, 2018

Summary

We developed the Computer-Aided Note and Diagnosis Interface (CANDI) to improve radiologist interpretation of medical images. CANDI facilitates collaborative annotation and evaluates how computer-aided diagnosis tools impact clinical decisions.

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Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Radiology informatics

Background:

Computer-Aided Diagnosis (CAD) algorithms have been used for decades, but their impact on radiologist interpretation remains debated.
Traditional CAD systems rely on engineered features, with mixed results regarding performance enhancement.
Deep learning models show promise but require substantial data and haven't been assessed in integrated human-AI decision systems.

Purpose of the Study:

To introduce the Computer-Aided Note and Diagnosis Interface (CANDI) for collaborative radiograph annotation.
To evaluate the impact of computer-aided diagnosis tools on radiologist interpretation and decision-making.
To facilitate the collection of diverse training data for AI models in medical imaging.

Main Methods:

Developed CANDI, comprising an annotation app for data collection (classification, segmentation, captioning) and an evaluation app for clinical trials.
The evaluation app employs randomization to assess the effect of CAD tool availability on radiologist performance.
Utilized machine learning and deep learning principles for algorithm development and evaluation.

Main Results:

CANDI enables collaborative annotation and provides a platform for rigorous evaluation of AI in clinical workflows.
The system facilitates the generation of high-quality training datasets for advanced AI models.
The randomized evaluation framework is designed to yield robust evidence on radiologist enhancement by CAD tools.

Conclusions:

CANDI offers a novel interface for advancing the integration of AI in diagnostic radiology.
The platform supports the development and validation of next-generation computer-aided diagnosis systems.
Future clinical trials using CANDI will clarify the role of AI in improving diagnostic accuracy and efficiency.