An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans

COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at identifying automatically lung parenchyma and lobes. Next, we combined such segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the obtained classification results with those obtained by three expert radiologists on a dataset consisting of 162 CT scans. Results showed a sensitivity of 90\% and a specificity of 93.5% for COVID-19 detection, outperforming those yielded by the expert radiologists, and an average lesion categorization accuracy of over 84%. Results also show that a significant role is played by prior lung and lobe segmentation that allowed us to enhance performance by over 20 percent points. The interpretation of the trained AI models, moreover, reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai

Human salivary peptides derived from histatins

Human saliva from a healthy donor was subjected to fractionation by gel chromatography and six pools were collected and analysed by MALDI-TOF-MS and HPLC-ESI-MS. Three peptides, corresponding to 888.3, 687.3, and 524.1 amu and SNYLYDN, YLYDN, and LYDN sequences (determined by automated Edman sequencing), were isolated from pool 4. YLYDN was not previously described in human saliva. The peptides show the common C-terminal sequence of histatin 3 and histatin 1. To exclude the possibility that the three peptides were an artifact of the purification procedure, nine samples of human saliva were collected from healthy donors, immediately acidified with 0.2% TFA, and analysed by RP-HPLC-ESI-MS. The three peptides were detected in all the analyzed samples. SNYLYDN was always found at a concentration higher than that of YLYDN and LYDN. A correlation analysis performed on quantitative data indicated that the three peptides derive only from histatin 3. Other already known histatins also were searched for in the chromatogram. Histatins 1, 2, 3, 5, 6, 7, 8, and 10 were observed, although not in all samples analyzed, whereas other minor histatins were not detected

Detection of Pulmonary tuberculosis: comparing MR imaging with HRCT

Abstract Background Computer Tomography (CT) is considered the gold standard for assessing the morphological changes of lung parenchyma. Although novel CT techniques have substantially decreased the radiation dose, radiation exposure is still high. Magnetic Resonance Imaging (MRI) has been established as a radiation- free alternative to CT for several lung diseases, but its role in infectious diseases still needs to be explored further. Therefore, the purpose of our study was to compare MRI with high resolution CT (HRCT) for assessing pulmonary tuberculosis. Methods 50 patients with culture-proven pulmonary tuberculosis underwent chest HRCT as the standard of reference and were evaluated by MRI within 24 h after HRCT. Altogether we performed 60 CT and MRI examinations, because 10 patients were also examined by CT and MRI at follow- up. Pulmonary abnormalities, their characteristics, location and distribution were analyzed by two readers who were blinded to the HRCT results. Results Artifacts did not interfere with the diagnostic value of MRI. Both HRCT and MRI correctly diagnosed pulmonary tuberculosis and identified pulmonary abnormalities in all patients. There were no significant differences between the two techniques in terms of identifying the location and distribution of the lung lesions, though the higher resolution of MRI did allow for better identification of parenchymal dishomogeneity, caseosis, and pleural or nodal involvement. Conclusion Technical developments and the refinement of pulse sequences have improved the quality and speed of MRI. Our data indicate that in terms of identifying lung lesions in non-AIDS patients with non- miliary pulmonary tuberculosis, MRI achieves diagnostic performances comparable to those obtained by HRCT but with better and more rapid identification of pulmonary tissue abnormalities due to the excellent contrast resolution.</p

Identification of the human salivary cystatin complex by the coupling of high-performance liquid chromatography and ion-trap mass spectrometry

Human salivary cystatins, five major (S, S1, S2, SA, SN) and two minor (C and D), are multifunctional proteins playing a different role in the oral environment. Salivary cystatin SN is able to effectively inhibit lysosomal cathepsins B, C, H and L and cystatin SA inhibits cathepsins C and L in vitro. These activities suggest, particularly for cystatin SN, an important role in the control of proteolytic events in vivo. Differently, cystatins S are involved, together with statherin, in the mineral balance of the tooth. Due to their distinct role, a reliable method for identification and quantification of the different cystatins, as well as of possible truncated and derived forms, could be helpful for the assessment of the status of the oral cavity. To this purpose high-performance liquid chromatography electrospray ionization mass spectrometry (HPLC-ESI MS) was applied to the analysis of human saliva obtained from healthy subjects. All known salivary cystatins, with the exception of cystatin C, were detected. Strong evidence was also obtained for the presence in saliva of post-translational modified isoforms of cystatins, which may be related to donor habits. Cystatin SN and cystatins S, S1 and S2 were well separated by HPLC-ESI MS coupling from other components and thus this approach can be successfully applied to their quantification

Identification of the human salivary cystatin complex by the coupling of high-performance liquid chromatography and ion-trap mass spectrometry

Human salivary cystatins, five major (S, S1, S2, SA, SN) and two minor (C and D), are multifunctional proteins playing a different role in the oral environment. Salivary cystatin SN is able to effectively inhibit lysosomal cathepsins B, C, H and L and cystatin SA inhibits cathepsins C and L in vitro. These activities suggest, particularly for cystatin SN, an important role in the control of proteolytic events in vivo. Differently, cystatins S are involved, together with statherin, in the mineral balance of the tooth. Due to their distinct role, a reliable method for identification and quantification of the different cystatins, as well as of possible truncated and derived forms, could be helpful for the assessment of the status of the oral cavity. To this purpose high-performance liquid chromatography electrospray ionization mass spectrometry (HPLC-ESI MS) was applied to the analysis of human saliva obtained from healthy subjects. All known salivary cystatins, with the exception of cystatin C, were detected. Strong evidence was also obtained for the presence in saliva of post-translational modified isoforms of cystatins, which may be related to donor habits. Cystatin SN and cystatins S, S1 and S2 were well separated by HPLC-ESI MS coupling from other components and thus this approach can be successfully applied to their quantification

An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans

COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world with exponential increasing of conrmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We rst propose a new segmentation module aimed at identifying automatically lung parenchyma and lobes. Next, we combined such segmentation network with classication networks for COVID-19 identication and lesion categorization. We compare the obtained classification results with those obtained by three expert radiologists on a dataset consisting of 162 CT scans. Results showed a sensitivity of 90% and a specicity of 93.5% for COVID-19 detection, outperforming those yielded by the expert radiologists, and an average lesion categorization accuracy of over 84%. Results also show that a signicant role is played by prior lung and lobe segmentation that allowed us to enhance performance by over 20 percent points. The interpretation of the trained AI models, moreover, reveals that the most signicant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the articial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http: // perceivelab. com/ covid-ai . The whole AI system is unique since, to the best of our knowledge, it is the first AI-based software, publicly available, that attempts to explain to radiologists what information is used by AI methods for making decision and that involves proactively them in the decision loop to further improve the COVID-19 understanding