Aurélio Campilho

Abstract

Abstract
The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55–0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61–0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients. © 2022, The Author(s).

Abstract
After publishing an in-depth study that analyzed the ability of computerized methods to assist or replace human experts in obtaining carotid intima-media thickness (CIMT) measurements leading to correct therapeutic decisions, here the same consortium joined to present technical outlooks on computerized CIMT measurement systems and provide considerations for the community regarding the development and comparison of these methods, including considerations to encourage the standardization of computerized CIMT measurements and results presentation. A multi-center database of 500 images was collected, upon which three manual segmentations and seven computerized methods were employed to measure the CIMT, including traditional methods based on dynamic programming, deformable models, the first order absolute moment, anisotropic Gaussian derivative filters and deep learning-based image processing approaches based on U-Net convolutional neural networks. An inter- and intra-analyst variability analysis was conducted and segmentation results were analyzed by dividing the database based on carotid morphology, image signal-to-noise ratio, and research center. The computerized methods obtained CIMT absolute bias results that were comparable with studies in literature and they generally were similar and often better than the observed inter- and intra-analyst variability. Several computerized methods showed promising segmentation results, including one deep learning method (CIMT absolute bias = 106 +/- 89 mu m vs. 160 +/- 140 mu m intra-analyst variability) and three other traditional image processing methods (CIMT absolute bias = 139 +/- 119 mu m, 143 +/- 118 mu m and 139 +/- 136 mu m). The entire database used has been made publicly available for the community to facilitate future studies and to encourage an open comparison and technical analysis

Abstract

Abstract
Purpose This study aims to investigate retinal and choroidal vascular reactivity to carbogen in central serous chorioretinopathy (CSC) patients. Methods An experimental pilot study including 68 eyes from 20 CSC patients and 14 age and sex-matched controls was performed. The participants inhaled carbogen (5% CO2 + 95% O-2) for 2 min through a high-concentration disposable mask. A 30 degrees disc-centered fundus imaging using infra-red (IR) and macular spectral domain optical coherence tomography (SD-OCT) using the enhanced depth imaging (EDI) technique was performed, both at baseline and after a 2-min gas exposure. A parametric model fitting-based approach for automatic retinal blood vessel caliber estimation was used to assess the mean variation in both arterial and venous vasculature. Choroidal thickness was measured in two different ways: the subfoveal choroidal thickness (SFCT) was calculated using a manual caliper and the mean central choroidal thickness (MCCT) was assessed using an automatic software. Results No significant differences were detected in baseline hemodynamic parameters between both groups. A significant positive correlation was found between the participants' age and arterial diameter variation (p < 0.001, r= 0.447), meaning that younger participants presented a more vasoconstrictive response (negative variation) than older ones. No significant differences were detected in the vasoreactive response between CSC and controls for both arterial and venous vessels (p = 0.63 and p = 0.85, respectively). Although the vascular reactivity was not related to the activity of CSC, it was related to the time of disease, for both the arterial (p = 0.02, r = 0.381) and venous (p = 0.001, r= 0.530) beds. SFCT and MCCT were highly correlated (r= 0.830, p < 0.001). Both SFCT and MCCT significantly increased in CSC patients (p < 0.001 and p < 0.001) but not in controls (p = 0.059 and 0.247). A significant negative correlation between CSC patients' age and MCCT variation (r = - 0.340, p = 0.049) was detected. In CSC patients, the choroidal thickness variation was not related to the activity state, time of disease, or previous photodynamic treatment. Conclusion Vasoreactivity to carbogen was similar in the retinal vessels but significantly higher in the choroidal vessels of CSC patients when compared to controls, strengthening the hypothesis of a choroidal regulation dysfunction in this pathology.