Automated detection of underdiagnosed medical conditions via opportunistic imaging
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908586469228544 |
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| author | Aali, Asad Johnston, Andrew Blankemeier, Louis Van Veen, Dave Derry, Laura T Svec, David Hom, Jason Boutin, Robert D. Chaudhari, Akshay S. |
| author_facet | Aali, Asad Johnston, Andrew Blankemeier, Louis Van Veen, Dave Derry, Laura T Svec, David Hom, Jason Boutin, Robert D. Chaudhari, Akshay S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11686 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated detection of underdiagnosed medical conditions via opportunistic imaging Aali, Asad Johnston, Andrew Blankemeier, Louis Van Veen, Dave Derry, Laura T Svec, David Hom, Jason Boutin, Robert D. Chaudhari, Akshay S. Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Automated detection of underdiagnosed medical conditions via opportunistic imaging |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.11686 |