Ultrasound imaging of the lung has played an important role in managing patients with COVID-19–associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.
The hitherto unexplored surface structural and dynamical properties of the thermoelectric material β-Cu2S chalcocite, are uncovered using ab initio molecular dynamics simulations at 450 K. The material exhibits a hybrid crystalline-liquid behavior, with the liquidlike dynamics of the Cu atoms and the crystalline order of the sulfur sublattice. The topmost nanoscale region of the material is predicted to undergo significant structural relaxation, resulting in a ∼10% increase in the distance between the topmost S-layers accompanied by an increased Cu density. Cu diffusion in the interlayer regions of the surface S-sublattice is enhanced (doubled) compared to the bulk value, and an underlying microscopic mechanism, entailing marked emergent surface-induced softening of the S-sublattice vibrational dynamics, is described.
Objectives To introduce an ultrasound‐based scoring system for radiation‐induced breast toxicity and test its reliability. Methods Breast ultrasound (BUS) was performed on 32 patients receiving breast radiotherapy (RT) to assess the radiation‐induced acute toxicity. For each patient, both the untreated and irradiated breasts were scanned at five locations: 12:00, 3:00, 6:00, 9:00, and tumor bed to evaluate for heterogenous responses to radiation within the entire breast. In total, 314 images were analyzed. Based on ultrasound findings such as skin thickening, dermis boundary irregularity, and subcutaneous edema, a 4‐level, Likert‐like grading scheme is proposed: none (G0), mild (G1), moderate (G2), and severe (G3) toxicity. Two ultrasound experts graded the severity of breast toxicity independently and reported the inter‐ and intra‐observer reliability of the grading system. Imaging findings were compared with standard clinical toxicity assessments using Common Terminology Criteria for Adverse Events (CTCAE). Results The inter‐observer Pearson correlation coefficient (PCC) was 0.87 (95% CI: 0.83–0.90, P < .001). For intra‐observer repeatability, the PCC of the repeated scores was 0.83 (95% CI: 0.78–0.87, P < .001). Imaging findings were compared with standard clinical toxicity assessments using CTCAE scales. The PCC between BUS scores and CTCAE results was 0.62 (95% CI: 0.35–0.80, P < .001). Among all locations, 6:00 and tumor bed showed significantly greater toxicity compared with 12:00 (P = .04). Conclusions BUS can investigate the cutaneous and subcutaneous tissue changes after RT. This BUS‐based grading system can complement subjective clinical assessments of radiation‐induced breast toxicity with cutaneous and subcutaneous sonographic information.
Proton therapy is a type of radiation therapy that can provide better dose distribution compared to photon therapy by delivering most of the energy at the end of range, which is called the Bragg peak (BP). The protoacoustic technique was developed to determine the BP locations in vivo, but it requires a large dose delivery to the tissue to obtain a high number of signal averaging (NSA) to achieve a sufficient signal-to-noise ratio (SNR), which is not suitable for clinical use. A novel deep learning-based technique has been proposed to denoise acoustic signals and reduce BP range uncertainty with much lower doses. Three accelerometers were placed on the distal surface of a cylindrical polyethylene (PE) phantom to collect protoacoustic signals. In total, 512 raw signals were collected at each device. Device-specific stack autoencoder (SAE) denoising models were trained to denoise the noise-containing input signals, which were generated by averaging only 1, 2, 4, 8, 16, or 24 raw signals(low NSA signals), while the cleansignals were obtained by averaging 192 raw signals (high NSA). Both supervised and unsupervised training strategies were employed, and the evaluation of the models was based on mean squared error (MSE), SNR, and BP range uncertainty. Overall, the supervised SAEs outperformed the unsupervised SAEs in BP range verification. For the high accuracy detector, it achieved a BP range uncertainty of 0.20 ± 3.44 mm by averaging over 8 raw signals, while for the other two low accuracy detectors, they achieved the BP uncertainty of 1.44 ± 6.45 mm and -0.23 ± 4.88 mm by averaging 16 raw signals, respectively. This deep learning-based denoising method has shown promising results in enhancing the SNR of protoacoustic measurements and improving the accuracy in BP range verification. It greatly reduces the dose and time for potential clinical applications.
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