Background

Recently, deep learning (DL) algorithms have received widespread popularity in various medical diagnostics. This study aimed to evaluate the diagnostic performance of DL models in the detection and classifying of pneumonia using chest X-ray (CXR) images.

Methods

PubMed, Embase, Scopus, Web of Science, and Google Scholar were searched in order to retrieve all studies that implemented a DL algorithm for discriminating pneumonia patients from healthy controls using CXR images. We used bivariate linear mixed models to pool diagnostic estimates including sensitivity (SE), specificity (SP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Also, the area under receiver operating characteristics curves (AUC) of the included studies was used to estimate the diagnostic value.

Results

The pooled SE, SP, PLR, NLR, DOR and AUC for DL in discriminating pneumonia CXRs from controls were 0.98 (95% confidence interval (CI): 0.96–0.99), 0.94 (95% CI: 0.90–0.96), 15.35 (95% CI: 10.04–23.48), 0.02 (95% CI: 0.01–0.04), 718.13 (95% CI: 288.45–1787.93), and 0.99 (95% CI: 0.98–100), respectively. The pooled SE, SP, PLR, NLR, DOR and AUC for DL in discriminating bacterial from viral pneumonia using CXR radiographs were 0.89 (95% CI: 0.79–0.94), 0.89 (95% CI: 0.78–0.95), 8.34 (95% CI: 3.75–18.55), 0.13 (95% CI: 0.06–0.26), 66.14 (95% CI: 17.34–252.37), and 0.95 (0.93–0.97).

Conclusion

DL indicated high accuracy performance in classifying pneumonia from normal CXR radiographs and also in distinguishing bacterial from viral pneumonia. However, major methodological concerns should be addressed in future studies for translating to the clinic.

中文翻译：

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使用胸部X射线图像自动检测肺炎的深度学习准确性：系统评价和荟萃分析。

背景

近年来，深度学习（DL）算法已在各种医学诊断中得到广泛普及。这项研究旨在评估DL模型在使用胸部X射线（CXR）图像进行肺炎的检测和分类中的诊断性能。

方法

检索PubMed，Embase，Scopus，Web of Science和Google Scholar，以检索所有实施DL算法的研究，这些算法使用CXR图像从健康对照中区分出肺炎患者。我们使用双变量线性混合模型来汇总诊断估计，包括敏感性（SE），特异性（SP），正似然比（PLR），负似然比（NLR）和诊断比值比（DOR）。同样，所纳入研究的接收器工作特性曲线下面积（AUC）用于估计诊断值。

结果

DL用于区分肺炎CXR与对照的SE，SP，PLR，NLR，DOR和AUC的合并值分别为0.98（95％置信区间（CI）：0.96-0.99），0.94（95％CI：0.90-0.96），15.35（ 95％CI：10.04–23.48），0.02（95％CI：0.01–0.04），718.13（95％CI：288.45–1787.93）和0.99（95％CI：0.98–100）。使用CXR射线照片对DL进行鉴别细菌与病毒性肺炎的细菌的合并SE，SP，PLR，NLR，DOR和AUC为0.89（95％CI：0.79–0.94），0.89（95％CI：0.78–0.95），8.34（95 CI的百分比：3.75-18.55），0.13（95％的CI：0.06-0.26），66.14（95％的CI：17.34-252.37）和0.95（0.93-0.97）。

结论

DL表示从正常CXR射线照片对肺炎进行分类以及区分细菌与病毒性肺炎的准确性较高。但是，主要的方法学问题应在将来的研究中解决，以便转诊至临床。