Böttcher Benjamin, van Assen Marly, Fari Roberto, von Knebel Doeberitz Philipp L, Kim Eun Young, Berkowitz Eugene A, Meinel Felix G, De Cecco Carlo N
Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, GA, USA.
Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Centre Rostock, Rostock, Germany.
Eur Radiol Exp. 2025 Jan 13;9(1):4. doi: 10.1186/s41747-024-00539-w.
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).
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.
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%).
CBIR software improves in-training radiologist diagnostic accuracy and confidence while reducing interpretation time in ILD assessment.
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.
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.
本回顾性研究旨在评估基于内容的图像检索（CBIR）应用程序对使用高分辨率计算机断层扫描（HRCT）评估间质性肺疾病（ILD）的诊断准确性和信心的影响。
28例经基于模式的ILD诊断验证的患者被分为两个相等的数据集（1和2）。两名放射科住院医师（第3年和第5年）和一名放射科专家在四个阶段对图像进行评估。数据集1用于A和C阶段，分别在使用和不使用CBIR软件的情况下评估诊断准确性和信心。数据集2用于B和D阶段，可选择使用CBIR，评估CBIR的使用时间和频率。在CT模式水平上评估准确性，将读者的诊断与参考诊断进行比较，并将CBIR结果与感兴趣区域（ROI）模式进行比较。
与不使用CBIR相比，使用CBIR时读者的诊断准确性和信心呈上升趋势（分别为53.6%对35.7%和50.0%对32.2%）。两个数据集中的阅读时间均显著减少（A对C：104秒对54秒，p<0.001；B对D：88.5秒对70秒，p=0.009），而使用CBIR软件时的研究时间增加（A对C：31秒对81秒，p=0.040）。CBIR结果显示基于模式的总体准确率较高，为73.4%。读者之间的比较表明，当使用多个ROI作为输入时，CBIR结果的准确率略高（77.7%对70.1%）。
CBIR软件可提高实习放射科医生的诊断准确性和信心，同时减少ILD评估中的解读时间。
基于内容的图像检索软件通过提高诊断准确性和信心，同时减少解读时间，改善了高分辨率CT中间质性肺疾病（ILD）的评估，特别是对放射科住院医师而言。这可为复杂病例提供教育益处并提高管理效率。
基于内容的图像检索（CBIR）软件提高了实习放射科医生在计算机断层扫描（CT）上评估间质性肺疾病（ILD）的诊断准确性和信心。CBIR应用程序提供了有关类似HRCT病例的浓缩信息，减少了ILD评估时间。CBIR算法受益于每个ILD病例多个感兴趣区域的输入。
