Detecting underdiagnosed medical conditions with opportunistic imaging

2024-09-18

AI Summary

This study addresses the underdiagnosis of occult conditions—sarcopenia, hepatic steatosis, and ascites—in routine abdominal CT scans (opportunistic imaging), quantifying diagnostic gaps between radiology reports and ICD coding. Method: Integrating deep learning–based image analysis with clinical natural language processing, we systematically evaluated 2,674 inpatient abdominal CT examinations to automatically extract imaging phenotypes and compare them against both structured ICD codes and unstructured radiology reports. Contribution/Results: We provide the first real-world, systematic quantification of opportunistic CT’s detection potential for these three conditions: ICD coding rates were only 0.5% for sarcopenia, 3.2% for hepatic steatosis, and 30.7% for ascites—demonstrating substantial underdiagnosis. Our framework enables scalable, imaging phenotype–driven risk stratification and supports refinement of clinical risk adjustment models and precision medicine initiatives.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Computer Vision: Medical and Biological Imaging

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Detecting underdiagnosed conditions via opportunistic CT scans
• Identifying discrepancies between imaging phenotypes and documentation
• Improving diagnostic precision using deep learning methods

Innovation

• Deep learning analyzes opportunistic CT scans
• Identifies discrepancies in imaging and documentation
• Enhances diagnostic precision in medicine