Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases
Abstract Background This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT). Methods Twenty-eight patients with v...
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SpringerOpen
2025-01-01
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| Series: | European Radiology Experimental |
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| Online Access: | https://doi.org/10.1186/s41747-024-00539-w |
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| author | Benjamin Böttcher Marly van Assen Roberto Fari Philipp L. von Knebel Doeberitz Eun Young Kim Eugene A. Berkowitz Felix G. Meinel Carlo N. De Cecco |
| author_facet | Benjamin Böttcher Marly van Assen Roberto Fari Philipp L. von Knebel Doeberitz Eun Young Kim Eugene A. Berkowitz Felix G. Meinel Carlo N. De Cecco |
| author_sort | Benjamin Böttcher |
| collection | DOAJ |
| description | Abstract Background This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT). Methods Twenty-eight patients with verified pattern-based ILD diagnoses were split into two equal datasets (1 and 2). The images were assessed by two radiology residents (3rd and 5th year) and one expert radiologist in four sessions. Dataset 1 was used for sessions A and C, assessing diagnostic accuracy and confidence with mandatory and without CBIR software. Dataset 2 was used for sessions B and D with optional CBIR use, assessing time spending and frequency of CBIR usage. Accuracy was assessed on the CT pattern level, comparing readers’ diagnoses with reference diagnoses and CBIR results with region-of-interest (ROI) patterns. Results Diagnostic accuracy and confidence of readers showed an increasing trend with CBIR use compared to no CBIR use (53.6% versus 35.7% and 50.0% versus 32.2%, respectively). Time for reading significantly decreased in both datasets (A versus C: 104 s versus 54 s, p < 0.001; B versus D: 88.5 s versus 70 s, p = 0.009), whereas time for research increased with CBIR software use (A versus C: 31 s versus 81 s, p = 0.040). CBIR results showed a high pattern-based accuracy of overall 73.4%. Comparison between readers indicates a slightly higher accuracy of CBIR results when more than one ROI was used as input (77.7% versus 70.1%). Conclusion CBIR software improves in-training radiologist diagnostic accuracy and confidence while reducing interpretation time in ILD assessment. Relevance statement Content-based image retrieval software improves the assessment of interstitial lung diseases (ILD) in high-resolution CT, especially for radiology residents, by increasing diagnostic accuracy and confidence while reducing interpretation time. This can provide educational benefits and more time-efficient management of complex cases. Key Points A content-based image retrieval (CBIR) software improves diagnostic accuracy and confidence for in-training radiologists for interstitial lung disease (ILD) assessment on computed tomography (CT). A CBIR application provides condensed information about similar HRCT cases reducing time for ILD assessment. CBIR algorithms benefit from the input of multiple regions of interest per ILD case. Graphical Abstract |
| format | Article |
| id | doaj-art-8aaa4b4325bd42e0b0fcbc9ddbd506db |
| institution | Kabale University |
| issn | 2509-9280 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Radiology Experimental |
| spelling | doaj-art-8aaa4b4325bd42e0b0fcbc9ddbd506db2025-01-19T12:09:33ZengSpringerOpenEuropean Radiology Experimental2509-92802025-01-019111210.1186/s41747-024-00539-wEvaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseasesBenjamin Böttcher0Marly van Assen1Roberto Fari2Philipp L. von Knebel Doeberitz3Eun Young Kim4Eugene A. Berkowitz5Felix G. Meinel6Carlo N. De Cecco7Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University HospitalDivision of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University HospitalDivision of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University HospitalDivision of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University HospitalDivision of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University HospitalDivision of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University HospitalInstitute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Centre RostockDivision of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University HospitalAbstract Background This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT). Methods Twenty-eight patients with verified pattern-based ILD diagnoses were split into two equal datasets (1 and 2). The images were assessed by two radiology residents (3rd and 5th year) and one expert radiologist in four sessions. Dataset 1 was used for sessions A and C, assessing diagnostic accuracy and confidence with mandatory and without CBIR software. Dataset 2 was used for sessions B and D with optional CBIR use, assessing time spending and frequency of CBIR usage. Accuracy was assessed on the CT pattern level, comparing readers’ diagnoses with reference diagnoses and CBIR results with region-of-interest (ROI) patterns. Results Diagnostic accuracy and confidence of readers showed an increasing trend with CBIR use compared to no CBIR use (53.6% versus 35.7% and 50.0% versus 32.2%, respectively). Time for reading significantly decreased in both datasets (A versus C: 104 s versus 54 s, p < 0.001; B versus D: 88.5 s versus 70 s, p = 0.009), whereas time for research increased with CBIR software use (A versus C: 31 s versus 81 s, p = 0.040). CBIR results showed a high pattern-based accuracy of overall 73.4%. Comparison between readers indicates a slightly higher accuracy of CBIR results when more than one ROI was used as input (77.7% versus 70.1%). Conclusion CBIR software improves in-training radiologist diagnostic accuracy and confidence while reducing interpretation time in ILD assessment. Relevance statement Content-based image retrieval software improves the assessment of interstitial lung diseases (ILD) in high-resolution CT, especially for radiology residents, by increasing diagnostic accuracy and confidence while reducing interpretation time. This can provide educational benefits and more time-efficient management of complex cases. Key Points A content-based image retrieval (CBIR) software improves diagnostic accuracy and confidence for in-training radiologists for interstitial lung disease (ILD) assessment on computed tomography (CT). A CBIR application provides condensed information about similar HRCT cases reducing time for ILD assessment. CBIR algorithms benefit from the input of multiple regions of interest per ILD case. Graphical Abstracthttps://doi.org/10.1186/s41747-024-00539-wArtificial intelligenceDiagnosis (computer-assisted)Lung diseases (interstitial)Tomography (x-ray computed) |
| spellingShingle | Benjamin Böttcher Marly van Assen Roberto Fari Philipp L. von Knebel Doeberitz Eun Young Kim Eugene A. Berkowitz Felix G. Meinel Carlo N. De Cecco Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases European Radiology Experimental Artificial intelligence Diagnosis (computer-assisted) Lung diseases (interstitial) Tomography (x-ray computed) |
| title | Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases |
| title_full | Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases |
| title_fullStr | Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases |
| title_full_unstemmed | Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases |
| title_short | Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases |
| title_sort | evaluation of a content based image retrieval system for radiologists in high resolution ct of interstitial lung diseases |
| topic | Artificial intelligence Diagnosis (computer-assisted) Lung diseases (interstitial) Tomography (x-ray computed) |
| url | https://doi.org/10.1186/s41747-024-00539-w |
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