Automated interpretation and analysis of bronchoalveolar lavage fluid

Comparison of Sysmex XN-V body fluid mode and deep-learning-based quantification with manual techniques for total nucleated cell count and differential count for equine bronchoalveolar lavage samples

BACKGROUND

Bronchoalveolar lavage (BAL) is a diagnostic method for the assessment of the lower respiratory airway health status in horses. Differential cell count and sometimes also total nucleated cell count (TNCC) are routinely measured by time-consuming manual methods, while faster automated methods exist. The aims of this study were to compare: 1) the Sysmex XN-V body fluid (BF) mode with the manual techniques for TNCC and two-part differential into mononuclear and polymorphonuclear cells; 2) the Olympus VS200 slide scanner and software generated deep-learning-based algorithm with manual techniques for four-part differential cell count into alveolar macrophages, lymphocytes, neutrophils, and mast cells. The methods were compared in 69 clinical BAL samples.

RESULTS

Incorrect gating by the Sysmex BF mode was observed on many scattergrams, therefore all samples were reanalyzed with manually set gates. For the TNCC, a proportional and systematic bias with a correlation of r = 0.79 was seen when comparing the Sysmex BF mode with manual methods. For the two-part differential count, a mild constant and proportional bias and a very small mean difference with moderate limits of agreement with a correlation of r = 0.84 and 0.83 were seen when comparing the Sysmex BF mode with manual methods. The Sysmex BF mode classified significantly more samples as abnormal based on the TNCC and the two-part differential compared to the manual method. When comparing the Olympus VS200 deep-learning-based algorithm with manual methods for the four-part differential cell count, a very small bias in the regression analysis and a very small mean difference in the difference plot, as well as a correlation of r = 0.85 to 0.92 were observed for all four cell categories. The Olympus VS200 deep-learning-based algorithm also showed better precision than manual methods for the four-part differential cell count, especially with an increasing number of analyzed cells.

CONCLUSIONS

The Sysmex XN-V BF mode can be used for TNCC and two-part differential count measurements after reanalyzing the samples with manually set gates. The Olympus VS200 deep-learning-based algorithm correlates well with the manual methods, while showing better precision and can be used for a four-part differential cell count.

Unveiling the Diagnostic Potential: A Comprehensive Review of Bronchoalveolar Lavage in Interstitial Lung Disease

This comprehensive review examines the diagnostic potential of bronchoalveolar lavage (BAL) in interstitial lung disease (ILD), emphasizing its accuracy and significance in various ILDs, including idiopathic pulmonary fibrosis (IPF), sarcoidosis, hypersensitivity pneumonitis, and connective tissue disease-associated ILD. The analysis underscores the importance of abnormalities in both cellular and non-cellular components of BAL fluid for precise ILD diagnosis. Recommendations advocate for the integration of BAL into clinical guidelines, a multidisciplinary diagnostic approach, and further standardization of procedures. Looking toward the future, ongoing research highlights technological advancements, biomarker discovery, and the integration of artificial intelligence in BAL interpretation. These developments not only promise to enhance ILD diagnosis but also offer prospects for a more personalized approach to patient management based on insightful patient stratification guided by BAL findings. This abstract encapsulates the key findings, recommendations, and future prospects identified in the review, providing a concise overview of the diagnostic potential of BAL in ILD.

An improved Yolov5s based on transformer backbone network for detection and classification of bronchoalveolar lavage cells

Biological tissue information of the lung, such as cells and proteins, can be obtained from bronchoalveolar lavage fluid (BALF), through which it can be used as a complement to lung biopsy pathology. BALF cells can be confused with each other due to the similarity of their characteristics and differences in the way sections are handled or viewed. This poses a great challenge for cell detection. In this paper, An Improved Yolov5s Based on Transformer Backbone Network for Detection and Classification of BALF Cells is proposed, focusing on the detection of four types of cells in BALF: macrophages, lymphocytes, neutrophils and eosinophils. The network is mainly based on the Yolov5s network and uses Swin Transformer V2 technology in the backbone network to improve cell detection accuracy by obtaining global information; the C3Ghost module (a variant of the Convolutional Neural Network architecture) is used in the neck network to reduce the number of parameters during feature channel fusion and to improve feature expression performance. In addition, embedding intersection over union Loss (EIoU_Loss) was used as a bounding box regression loss function to speed up the bounding box regression rate, resulting in higher accuracy of the algorithm. The experiments showed that our model could achieve mAP of 81.29% and Recall of 80.47%. Compared to the original Yolov5s, the mAP has improved by 3.3% and Recall by 3.67%. We also compared it with Yolov7 and the newly launched Yolov8s. mAP improved by 0.02% and 2.36% over Yolov7 and Yolov8s respectively, while the FPS of our model was higher than both of them, achieving a balance of efficiency and accuracy, further demonstrating the superiority of our model.

A comparative study of attention mechanism based deep learning methods for bladder tumor segmentation

BACKGROUND

Artificial intelligence aided tumor segmentation has been applied in various medical scenarios and showed effectiveness in helping physicians observe the potential malignant tissues. However, little research has been conducted for the cystoscopic image segmentation problem.

METHODS

This paper provided a comprehensive comparison of various attention modules for improving the bladder tumor segmentation performance by utilizing the cystoscopic images from Peking University Third Hospital within 2017-2022. Furthermore, this paper presented an attention mechanism based cystoscopic images segmentation (ACS) model, which was featured by the following points: (1) A mixed attention module including both the channel and spatial attention modules was integrated in the encoder-decoder path, which helped to exploit the global information of the tumor area more effectively. (2) A guidance and fusion attention module was introduced in the skip connection part, facilitating the integration of the high-level semantic features with low-level fine-grained features and the discarding of irrelevant features. (3) An inception attention module was added to enhance the feature expression in the scale of pixel level, so as to better discriminate multi-scale targets.

RESULTS

The proposed ACS model showed obviously better tumor segmentation performance than the compared models, with Dice of 82.7% and MIoU of 69% achieved.

CONCLUSIONS

The proposed ACS model achieved significantly better diagnostic performance than the previous bladder tumor segmentation method based on U-Net. Our ACS model is expected to be a useful support tool to assist the tumor segmentation under cystoscopy.