Innovative AI techniques to aid clinical diagnosis of complex lung pathologies in ultrasound images

Abstract

Lung ultrasound (LUS) has recently gained increasing interest as a reliable method for point of care (POC) diagnostics and management of lung diseases. LUS is easily accessible, has no radiation-related risks and is portable, so it does not require relocating the patient, minimising the risk of further infection. This research aims to develop efficient and automated methods for lung pathology diagnosis using AI to support clinicians. We have introduced a binary classifier based on a state-of-the-art Swin Transformer to discriminate between LUS clips of healthy patients and patients with interstitial lung disease (ILD). Differently from previous approaches, this is better aligned with the current clinical assessments of ILD since it evaluates LUS clips instead of single frames; and does not require any additional annotations from clinicians, since its training is based only on the already available medical report. Furthermore, we propose an unsupervised deep learning approach (based on a generative adversarial network) to convert in real-time US volumes to MRI-like volumes of the thoracic region. This approach can be used to spatially localise the US probe with respect to the MRI in real-time and gather anatomical contextual information of the imaged region, providing thus guidance to clinicians.