Automated interpretation and analysis of bronchoalveolar lavage fluid

Background: The cytological analysis of bronchoalveolar lavage fluid (BALF) plays an essential role in the dif-ferential 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 expe-rienced practitioners.Conclusion: The program can automatically provide the cytological background of BALF and augment clinical decision-making for clinicians.

Automated summary

This study aims to apply deep learning to the automated interpretation and analysis of bronchoalveolar lavage fluid (BALF) cytology. By using a convolutional neural network, our model successfully detected most cells in BALF specimens and outperformed experienced practitioners in clinical tests. The automated interpretation provided by the program can augment clinical decision-making for clinicians.