System of digital vision for X-ray lung pathology and foreign body detection


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Goal and objectives: to develop an effective computer vision system for detecting pathology and foreign bodies of medical and unmedical origin on plain chest radiographs. Material and methods: in order to build the model, aggregation of convolutional artificial neural networks of the InceptionV3, ResNet-50 and GlobalAveragePooling architectures was used. The outputs from all models were combined into a single vector and used as input for the boosting model, which was used as the XGBoost model. For training and testing the system, 276840 anonymized chest x-ray in a frontal view were used. Results. A number of computer vision models have been developed for the analysis of X-ray examinations of the lungs. To achieve a satisfactory balance between the prediction accuracy indicators, a decision threshold of 0.4 was chosen empirically. Such a balance makes it possible to reduce the number of false-negative model predictions and increase the number of cases where pathological changes are suspected. Conclusions. The developed model of computer vision can be considered as an effective assistant to the radiologists in the analysis of chest x-ray images, allowing them to create a list of priority images for immediate and delayed analysis and description.

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作者简介

E. Zhukov

Care Mentor AI

Moscow

D. Blinov

Care Mentor AI

Email: d.blinov@cmai.team
MD Moscow

V. Leontiev

Inozemtzev Moscow State Clinical Hospital

Moscow

P. Gavrilov

Saint-Petersburg Research Institute of Phthisiopulmonology

Candidate of Medical Sciences Moscow

U. Smolnikova

Saint-Petersburg Research Institute of Phthisiopulmonology

Moscow

E. Blinova

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Professor, MD Moscow

I. Kamishanskaya

Saint-Petersburg State University

Candidate of Medical Sciences

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