Development and Validation of a Highly Generalizable Deep Learning Pulmonary Embolism Detection Algorithm

AbstractSeveral algorithms have been developed for the detection of pulmonary embolism, though generalizability and bias remain potential weaknesses due to small sample size and sample homogeneity. We developed and validated a highly generalizable deep-learning algorithm, Emboleye, for the detection of PE by using a large and diverse dataset, which included 30,574 computed tomography (CT) exams sourced from over 2,000 hospital sites. On angiography exams, Emboleye demonstrates an AUROC of 0.79 with a specificity of 0.99 while maintaining a sensitivity of 0.37 and PPV of 0.77. On non-angiography CT exams, Emboleye demonstrates an AUROC of 0.77 with a specificity of 0.99 while maintaining a sensitivity of 0.18 and PPV of 0.35.

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Development and Validation of Suspicious Liver Lesion Deep Learning Algorithm for Computed Tomography

10.1101/2021.12.15.21267873 ◽

2021 ◽

Author(s):

Jacob Johnson ◽

Kaneel Senevirathne ◽

Lawrence Ngo

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Small Sample Size ◽

Liver Lesion ◽

The United States ◽

Detection Algorithm ◽

Small Sample ◽

Lesion Detection ◽

Liver Lesions ◽

Deep Learning Algorithm

In this work, we report the results of a deep-learning based liver lesion detection algorithm. While several liver lesion segmentation and classification algorithms have been developed, none of the previous work has focused on detecting suspicious liver lesions. Furthermore, their generalizability remains a pitfall due to their small sample size and sample homogeneity. Here, we developed and validated a highly generalizable deep-learning algorithm for detection of suspicious liver lesions. The algorithm was trained and tested on a diverse dataset containing CT exams from over 2,000 hospital sites in the United States. Our final model achieved an AUROC of 0.84 with a specificity of 0.99 while maintaining a sensitivity of 0.33.

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Development and Validation of a Deep Learning Algorithm to Evaluate Endoscopic Disease Activity of Ulcerative Colitis.

Case Medical Research ◽

10.31525/ct1-nct03973437 ◽

2019 ◽

Author(s):

Keyword(s):

Ulcerative Colitis ◽

Deep Learning ◽

Disease Activity ◽

Learning Algorithm ◽

Deep Learning Algorithm ◽

Development And Validation

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Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

Cancers ◽

10.3390/cancers13040652 ◽

2021 ◽

Vol 13 (4) ◽

pp. 652 ◽

Cited By ~ 2

Author(s):

Carlo Augusto Mallio ◽

Andrea Napolitano ◽

Gennaro Castiello ◽

Francesco Maria Giordano ◽

Pasquale D'Alessio ◽

...

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Deep Learning ◽

Convolutional Neural Network ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitor ◽

Learning Algorithm ◽

Checkpoint Inhibitor ◽

Deep Convolutional Neural Network ◽

Deep Learning Algorithm

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

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