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Stroke, the second leading cause of mortality globally, predominantly results from ischemic conditions. Immediate attention and diagnosis, related to the characterization of brain lesions, play a crucial role in patient prognosis. Standard stroke protocols include an initial evaluation from a non-contrast CT to discriminate between hemorrhage and ischemia. However, non-contrast CTs lack sensitivity in detecting subtle ischemic changes in this phase. Alternatively, diffusion-weighted MRI studies provide enhanced capabilities, yet are constrained by limited availability and higher costs. Hence, we idealize new approaches that integrate ADC stroke lesion findings into CT, to enhance the analysis and accelerate stroke patient management. This study details a public challenge where scientists applied top computational strategies to delineate stroke lesions on CT scans, utilizing paired ADC information. Also, it constitutes the first effort to build a paired dataset with NCCT and ADC studies of acute ischemic stroke patients. Submitted algorithms were validated with respect to the references of two expert radiologists. The best achieved Dice score was 0.2 over a test study with 36 patient studies. Despite all the teams employing specialized deep learning tools, results reveal limitations of computational approaches to support the segmentation of small lesions with heterogeneous density., (© 2024. The Author(s).) more...

Background: Asian American (AA) community leaders, Native Hawaiian/Pacific Islander (NH/PI) community leaders, and allies in the United States Pacific Northwest expressed concern that there are families and children from AA communities and NH/PI communities who experience and witness acts of xenophobia and racism. This can cause racial trauma. The long-time practice of aggregating AA and NH/PI data contributes to erasure and makes it challenging to advance health equity, such as allocating resources. According to AAPI Data's long-awaited report in June 2022, there are over 24 million AAs and 1.6 million NHs/PIs in the United States, growing by 40% and 30%, respectively, between 2010 and 2020. Philanthropic investments have not kept up with this substantive increase. The National Academies of Sciences, Engineering, and Medicine emphasized the need for effective partnerships to advance the health and well-being of individuals and communities in antiracism and system-level research., Objective: The aim of this community-based participatory research qualitative description study was to identify perceptions and experiences regarding racial discrimination, race-based stress, and racial trauma; intergenerational healing and resiliency; and sharing the body with science from key informants of an academic and community partnership to inform antiracism coalition work. This partnership includes academic researchers and community leaders from community-based organizations and a health care organization serving immigrant and marginalized communities, including AAs and NHs/PIs in the United States Pacific Northwest., Methods: In total, 10 key informants joined 1 of 2 participatory group discussions via videoconference for 2 hours in 2022. We used a semistructured and open-ended group interview guide. A qualitative participatory group-level assessment was conducted with the key informants and transcribed. Interpretations and meanings of the main points and the main themes were reflected upon, clarified, and verified with the key informants in real time. The field note-based data transcripts were manually coded using conventional content analysis. Reflexivity was used., Results: There were 6 main themes: prejudice plus power in racism definition and working in solidarity to counter lateral oppression/false sense of security, microaggression as multilayers, "not assimilationist by nature" and responding differently to white superiority, intergenerational- and identity-related trauma, what is healing among People of Color and through a lens of resiliency and intergenerational connection and knowledge, and mistrust and fear in the research and health care systems surrounding intentions of the body., Conclusions: The themes highlight the importance of internal and intergenerational healing from racial trauma and the need for solidarity among communities of color to combat white supremacy and colonization. This work was foundational in an ongoing effort to dismantle racism and uplift the community voice through a cross-sector academic and community partnership to inform antiracism coalition work., (©Connie Kim Yen Nguyen-Truong, Sara F Waters, Meenakshi Richardson, Natasha Barrow, Joseph Seia, Deborah U Eti, Keara Funchess Rodela. Originally published in the Asian/Pacific Island Nursing Journal (https://apinj.jmir.org), 11.01.2023.) more...

Introduction: Pasifika (Native Hawaiian and Pacific Islander) people living in the United States experience health, economic, and social inequities, and a disproportionate burden of COVID-19 cases and deaths. This study examines employment among Pasifika living in the 10 US states with the largest Pasifika populations during the COVID-19 pandemic., Methods: We use the Current Population Survey to examine racial differences in employment status, paid work from home (PWFH), and industry telework friendliness. We use data from the Washington Office of Fiscal Management and the Washington State (WA) Employment Security Department to examine county-level unemployment claims., Results: Nationally, Pasifika did not self-report unemployment significantly more than Black, Latino, Asian, and American Indian/Alaska Native respondents, but in WA counties with high Pasifika concentrations, unemployment insurance claim rates were higher compared with all other racial groups, particularly Whites and Asians. Surprisingly, Pasifika had more PWFH opportunities, but worked in less telework-friendly industries nationally., Discussion: This study demonstrates the complexity of employment among Pasifika during the COVID-19 pandemic. The findings correspond with national reports of racialized communities impacted by unemployment, including Pasifika. Marginally significant differences in unemployment nationally may be due to Pasifika working largely in essential industries requiring workplace attendance., Health Equity Implications: Although overlooked or overshadowed by size, our findings highlight the need for continued advocacy to support data disaggregation and Pasifika data sovereignty. This can be achieved through collaborations between researchers as well as local and community organizations to address data needs of Pasifika communities., Competing Interests: No competing financial interests exist., (© Santino G. Camacho et al., 2022; Published by Mary Ann Liebert, Inc.) more...

Purpose: To investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support., Materials and Methods: An AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system., Results: Discrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, p < 0.0001). When working with AI support, physicians increased their diagnostic sensitivity from 47% to 61% (p < 0.001), although specificity decreased from 79% to 75% (p = 0.007)., Conclusions: Our results suggest interpreting chest radiographs (CXR) supported by AI, increases physician diagnostic sensitivity for COVID-19 detection. This approach involving a human-machine partnership may help expedite triaging efforts and improve resource allocation in the current crisis., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mauricio F. Farez has received professional travel/accommodations stipends from Merck-Serono Argentina, Teva Argentina and Novartis Argentina. The rest of the authors declare no competing interests., (© 2020 Elsevier B.V.) more...