Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.
T he scientific, academic, medical and data science communities have come together in the face of the COVID-19 pandemic crisis to rapidly assess novel paradigms in artificial intelligence (AI) that are rapid and secure, and potentially incentivize data sharing and model training and testing without the usual privacy and data ownership hurdles of conventional collaborations 1,2 . Healthcare providers, researchers and industry have pivoted their focus to address unmet and critical clinical needs created by the crisis, with remarkable results [3][4][5][6][7][8][9] . Clinical trial recruitment has been expedited and facilitated by national regulatory bodies and an international cooperative spirit 10-12 . The data analytics and AI disciplines have always fostered open
Background
The ablation index (AI) is reported to be useful for a durable pulmonary vein isolation (PVI). However, there have been no studies investigating the relationship between the power, contact force (CF), AI, and steam pops.
Methods
Using an in vitro model, ablation energy was delivered until a steam pop occurred and the time to the steam pop and AI when the steam pop occurred were measured. The experiment was performed with a combination of various powers (20, 30, 40, and 50 W) and contact forces (CFs) (10, 30, and 50 g) 20 times for each setting. The analysis consisted of two protocols. The first protocol was a comparison between the ablation power and several parameters under the same CF (10, 30, and 50 g). The second protocol was a comparison between the CF and several parameters under the same power (20, 30, 40, and 50 W). The correlation between the lesion formation and ablation parameters was evaluated.
Results
The AI value when steam pops occurred varied depending on the ablation settings. All AI median values were <500 under an ablation power of 50 W. On other hand, the median ablation time up to the steam pop was more than 46 seconds, but all median values of the AI were more than 550 under an ablation with 20 W.
Conclusions
The AI cannot predict steam pops. A low power and long duration ablation could obtain a high AI value. However, high‐power ablation could not obtain a high AI value because of an early occurrence of steam pops.
Objectives
The symptoms of Coronavirus disease 2019 (COVID-19) vary among patients. The aim of this study was to investigate the clinical manifestation and disease duration in young versus elderly patients.
Methods
We retrospectively analyzed 187 patients (87 elderly and 100 young patients) with confirmed COVID-19. The clinical characteristics and chest computed tomography (CT) extent as defined by a score were compared between the two groups.
Results
The numbers of asymptomatic cases and severe cases were significantly higher in the elderly group (elderly group vs. young group; asymptomatic cases, 31 [35.6%] vs. 10 [10%], p < 0.0001; severe cases, 25 [28.7%] vs. 8 [8.0%], p = 0.0002). The proportion of asymptomatic patients and severe patients increased across the 10-year age groups. There was no significant difference in the total CT score and number of abnormal cases. A significant positive correlation between the disease duration and patient age was observed in asymptomatic patients (ρ = 0.4570, 95% CI 0.1198–0.6491, p = 0.0034).
Conclusions
Although the extent of lung involvement did not have a significant difference between the young and elderly patients, elderly patients were more likely to have severe clinical manifestations. Elderly patients were also more likely to be asymptomatic and a source of COVID-19 viral shedding.
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