Automated Assessment of Image Quality in 2D Echocardiography Using Deep Learning

Echocardiography is the most used modality for assessing cardiac functions. The reliability of the echocardiographic measurements, however, depends on the quality of the images. Currently, the method of image quality assessment is a subjective process, where an echocardiography specialist visually inspects the images. An automated image quality assessment system is thus required. Here, we have reported on the feasibility of using deep learning for developing such automated quality scoring systems. A scoring system was proposed to include specific quality attributes for on-axis, contrast/gain and left ventricular (LV) foreshortening of the apical view. We prepared and used 1,039 echocardiographic patient datasets for model development and testing. Average accuracy of at least 86% was obtained with computation speed at 0.013ms per frame which indicated the feasibility for real-time deployment

A digital score of peri‐epithelial lymphocytic activity predicts malignant transformation in oral epithelial dysplasia

Oral squamous cell carcinoma (OSCC) is amongst the most common cancers, with more than 377,000 new cases worldwide each year. OSCC prognosis remains poor, related to cancer presentation at a late stage, indicating the need for early detection to improve patient prognosis. OSCC is often preceded by a premalignant state known as oral epithelial dysplasia (OED), which is diagnosed and graded using subjective histological criteria leading to variability and prognostic unreliability. In this work, we propose a deep learning approach for the development of prognostic models for malignant transformation and their association with clinical outcomes in histology whole slide images (WSIs) of OED tissue sections. We train a weakly supervised method on OED cases (n = 137) with malignant transformation (n = 50) and mean malignant transformation time of 6.51 years (±5.35 SD). Stratified five-fold cross-validation achieved an average area under the receiver-operator characteristic curve (AUROC) of 0.78 for predicting malignant transformation in OED. Hotspot analysis revealed various features of nuclei in the epithelium and peri-epithelial tissue to be significant prognostic factors for malignant transformation, including the count of peri-epithelial lymphocytes (PELs) (p < 0.05), epithelial layer nuclei count (NC) (p < 0.05), and basal layer NC (p < 0.05). Progression-free survival (PFS) using the epithelial layer NC (p < 0.05, C-index = 0.73), basal layer NC (p < 0.05, C-index = 0.70), and PELs count (p < 0.05, C-index = 0.73) all showed association of these features with a high risk of malignant transformation in our univariate analysis. Our work shows the application of deep learning for the prognostication and prediction of PFS of OED for the first time and offers potential to aid patient management. Further evaluation and testing on multi-centre data is required for validation and translation to clinical practice. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Echocardiographic phase detection using neural networks

Accurate identification of end-diastolic (ED) and end-systolic (ES) frames in echocardiographic cine loops is essential when measuring cardiac function. Manual selection by human experts is challenging and error prone. We present a deep neural network trained and tested on multi-centre patient data for accurate phase detection in apical four-chamber videos of arbitrary length, spanning several heartbeats, with performance indistinguishable from that of human experts

A neural architecture search based framework for segmentation of epithelium, nuclei and oral epithelial dysplasia grading

Oral epithelial dysplasia (OED) is a pre-cancerous histopathological diagnosis given to a range of oral lesions. Architectural, cytological and histological features of OED can be modelled through the segmentation of full epithelium, individual nuclei and stroma (connective tissues) to provide significant diagnostic features. In this paper, we explore a customised neural architecture search (NAS) based method for optimisation of an efficient architecture for segmentation of the full epithelium and individual nuclei in pathology whole slide images (WSIs). Our initial experimental results show that the NAS-derived architecture achieves 93.5% F1-score for the full epithelium segmentation and 94.5% for nuclear segmentation outperforming other state-of-the-art models. Accurate nuclear segmentation allows us to perform quantitative statistical and morphometric feature analyses of the segmented nuclei within regions of interest (ROIs) of multi-gigapixel whole-slide images (WSIs). We show that a random forest model using these features can differentiate between low-risk and high-risk OED lesions