Automated interpretation and analysis of bronchoalveolar lavage fluid

Abstract

Background: The cytological analysis of bronchoalveolar lavage fluid (BALF) plays an essential role in the differential diagnosis of respiratory diseases. In recent years, deep learning has demonstrated excellent performance in image processing and object recognition.

Objectives: We aim to apply deep learning to the automated interpretation and analysis of BALF.

Method: Visual images were acquired using an automated biological microscopy platform. We propose a three-step algorithm to automatically interpret BALF cytology based on a convolutional neural network (CNN). The clinical value was evaluated at the patient level.

Results: Our model successfully detected most cells in BALF specimens and achieved a sensitivity, precision, and F1 score of over 0.9 for most cell types. In two tests in the clinical context, the algorithm outperformed experienced practitioners.

Conclusion: The program can automatically provide the cytological background of BALF and augment clinical decision-making for clinicians.

Keywords: Artificial intelligence; Bronchoalveolar lavage fluid; Critical care; Deep learning.