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1.
IEEE J Biomed Health Inform ; PP2022 Sep 22.
Article in English | MEDLINE | ID: covidwho-2037819

ABSTRACT

The COVID-19 pandemic has highlighted the need for a tool to speed up triage in ultrasound scans and provide clinicians with fast access to relevant information. To this end, we propose a new unsupervised reinforcement learning (RL) framework with novel rewards to facilitate unsupervised learning by avoiding tedious and impractical manual labelling for summarizing ultrasound videos. The proposed framework is capable of delivering video summaries with classification labels and segmentations of key landmarks which enhances its utility as a triage tool in the emergency department (ED) and for use in telemedicine. Using an attention ensemble of encoders, the high dimensional image is projected into a low dimensional latent space in terms of: a) reduced distance with a normal or abnormal class (classifier encoder), b) following a topology of landmarks (segmentation encoder), and c) the distance or topology agnostic latent representation (autoencoders). The summarization network is implemented using a bi-directional long short term memory (Bi-LSTM) which utilizes the latent space representation from the encoder. Validation is performed on lung ultrasound (LUS), that typically represent potential use cases in telemedicine and ED triage acquired from different medical centers across geographies (India and Spain). The proposed approach trained and tested on 126 LUS videos showed high agreement with the ground truth with an average precision of over 80% and average F1 score of well over 44 ±1.7 %. The approach resulted in an average reduction in storage space of 77% which can ease bandwidth and storage requirements in telemedicine.

3.
J Ultrasound Med ; 40(9): 1879-1892, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-956716

ABSTRACT

OBJECTIVES: To develop a consensus statement on the use of lung ultrasound (LUS) in the assessment of symptomatic general medical inpatients with known or suspected coronavirus disease 2019 (COVID-19). METHODS: Our LUS expert panel consisted of 14 multidisciplinary international experts. Experts voted in 3 rounds on the strength of 26 recommendations as "strong," "weak," or "do not recommend." For recommendations that reached consensus for do not recommend, a fourth round was conducted to determine the strength of those recommendations, with 2 additional recommendations considered. RESULTS: Of the 26 recommendations, experts reached consensus on 6 in the first round, 13 in the second, and 7 in the third. Four recommendations were removed because of redundancy. In the fourth round, experts considered 4 recommendations that reached consensus for do not recommend and 2 additional scenarios; consensus was reached for 4 of these. Our final recommendations consist of 24 consensus statements; for 2 of these, the strength of the recommendations did not reach consensus. CONCLUSIONS: In symptomatic medical inpatients with known or suspected COVID-19, we recommend the use of LUS to: (1) support the diagnosis of pneumonitis but not diagnose COVID-19, (2) rule out concerning ultrasound features, (3) monitor patients with a change in the clinical status, and (4) avoid unnecessary additional imaging for patients whose pretest probability of an alternative or superimposed diagnosis is low. We do not recommend the use of LUS to guide admission and discharge decisions. We do not recommend routine serial LUS in patients without a change in their clinical condition.


Subject(s)
COVID-19 , Inpatients , Canada , Consensus , Humans , Lung/diagnostic imaging , SARS-CoV-2
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