Automated detection of underdiagnosed medical conditions via opportunistic imaging

TL;DR

This study leverages deep learning on routine abdominal CT scans to identify underdiagnosed conditions like sarcopenia, hepatic steatosis, and ascites, revealing significant underreporting in clinical documentation and ICD coding.

Contribution

It demonstrates the potential of opportunistic CT imaging combined with deep learning to improve detection of underdiagnosed conditions and enhance diagnostic accuracy.

Findings

Low ICD coding rates for diagnosed conditions (0.5%-30.7%)

Deep learning can identify underdiagnosed conditions from routine scans

Opportunistic CT improves diagnostic precision and risk adjustment

Abstract

Abdominal computed tomography (CT) scans are frequently performed in clinical settings. Opportunistic CT involves repurposing routine CT images to extract diagnostic information and is an emerging tool for detecting underdiagnosed conditions such as sarcopenia, hepatic steatosis, and ascites. This study utilizes deep learning methods to promote accurate diagnosis and clinical documentation. We analyze 2,674 inpatient CT scans to identify discrepancies between imaging phenotypes (characteristics derived from opportunistic CT scans) and their corresponding documentation in radiology reports and ICD coding. Through our analysis, we find that only 0.5%, 3.2%, and 30.7% of scans diagnosed with sarcopenia, hepatic steatosis, and ascites (respectively) through either opportunistic imaging or radiology reports were ICD-coded. Our findings demonstrate opportunistic CT's potential to enhance diagnostic precision and accuracy of risk adjustment models, offering advancements in precision medicine.