We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system consists of two deep-learning-based models: a quality assessment module that automates predictions of image quality, and a diagnosis assistance module that determines the likelihood-of-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for training the quality assessment classifier. The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module. Using more than 25,000 ultrasound images from 37 COVID-19-positive patients scanned at two hospitals, plus 12 control cases, this study demonstrates the feasibility of using the proposed machine learning approach. We report an accuracy of 86% when classifying between sufficient and insufficient quality images by the quality assessment module. For data of sufficient quality – as determined by the quality assessment module – the mean classification accuracy, sensitivity, and specificity in detecting COVID-19-positive cases were 0.95, 0.91, and 0.97, respectively, across five holdout test data sets unseen during the training of any networks within the proposed system. Overall, the integration of the two modules yields accurate, fast, and practical acquisition guidance and diagnostic assistance for patients with suspected respiratory conditions at pointof- care.
The International Photoacoustic Standardisation Consortium (IPASC) emerged from SPIE 2018, established to drive consensus on photoacoustic system testing. As photoacoustic imaging (PAI) matures from research laboratories into clinical trials, it is essential to establish best-practice guidelines for photoacoustic image acquisition, analysis and reporting, and a standardised approach for technical system validation. The primary goal of the IPASC is to create widely accepted phantoms for testing preclinical and clinical PAI systems. To achieve this, the IPASC has formed five working groups (WGs). The first and second WGs have defined optical and acoustic properties, suitable materials, and configurations of photoacoustic image quality phantoms. These phantoms consist of a bulk material embedded with targets to enable quantitative assessment of image quality characteristics including resolution and sensitivity across depth. The third WG has recorded details such as illumination and detection configurations of PAI instruments available within the consortium, leading to proposals for system-specific phantom geometries. This PAI system inventory was also used by WG4 in identifying approaches to data collection and sharing. Finally, WG5 investigated means for phantom fabrication, material characterisation and PAI of phantoms. Following a pilot multi-centre phantom imaging study within the consortium, the IPASC settled on an internationally agreed set of standardised recommendations and imaging procedures. This leads to advances in: (1) quantitative comparison of PAI data acquired with different data acquisition and analysis methods; (2) provision of a publicly available reference data set for testing new algorithms; and (3) technical validation of new and existing PAI devices across multiple centres.
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