Evaluation of an Artificial Intelligence Coronary Artery Calcium Scoring Model from Computed Tomography

OBJECTIVES: Coronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC. METHODS: Fully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1–10, 11–100, 101–400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers). RESULTS: Of 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman’s r = 0.90, 95% confidence interval [CI] 0.89–0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98–0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland–Altman analysis demonstrated little difference (1.69, 95% limits of agreement: −41.22, 44.60) and there was almost excellent agreement (Cohen’s κ = 0.90, 95% CI 0.88–0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan. CONCLUSIONS: This artificial intelligence–based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation. KEY POINTS: • Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)–based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-09028-3

Constraints to effective comanagement of New Zealand's customary fisheries: experiences of the East Otago Taiāpure

Centralization of fisheries management within large-scale, colonial governing bodies can remove access and management rights of Indigenous communities and deplete marine resources through a mismatch in bioecological and managerial scales. Management of pāua (blackfoot abalone, Haliotis iris ) in Aotearoa New Zealand exemplifies this transition from small-scale fisheries management by tangata whenua (local Indigenous people with historical claim to the land, Māori) to central government regulation and subsequent overexploitation. Comanagement strategies have the potential to address degradation of biological and cultural diversity by returning management to local scales and authority to local people. New Zealand's customary fisheries management legislation aims to facilitate such a devolution of management back to tangata whenua through the establishment of Taiāpure Local Fisheries and Mātaitai Reserves. However, local management systems can remain constrained by the wider governance structures that encompass them. These constraints are discussed in relation to a management proposal for pāua harvesting made by the East Otago Taiāpure Management Committee. The proposal aimed to return fishing practices to a customary method, providing greater protection for declining pāua populations while allowing a small harvest to continue. After a long and protracted application process, central government did not support the proposed regulation. This opposition demonstrated many of the constraints that local management committees face as they endeavor to operate within the confines of broader legal frameworks: conflicting worldviews, inequitable power sharing, perceived inferiority of Indigenous customs, requirements for conventional science, and navigation of bureaucratic processes. Insights are also drawn from another small-scale abalone fishery (ormer, Haliotis tuberculata ) in the Channel Islands, for which the desired regulation has been in place for over three decades

Who is catching what? A survey of recreational fishing effort and success on the East Otago Taiāpure

He Kohinga Tangahau is the research report series of Te Tiaki Mahinga Kai, a national coalition of tangata kaitiaki, researchers and managers dedicated to sustained enhancement of the cultural, economic, social and environmental well being of Māori and New Zealand as a whole through the application of mātauranga and science associated with mahinga kai to modern customary fisheries practices. See www.mahingakai.org.nz for a detailed description of the kaupapa. He Kohinga Rangahau means “the gathering together of research findings”. The report may be used and cited by anyone with due acknowledgement to Te Rūnanga o Ngāi Tahu who are directing and funding the overall project

Who is catching what? A survey of recreational fishing effort and success on the East Otago Taiāpure

He Kohinga Tangahau is the research report series of Te Tiaki Mahinga Kai, a national coalition of tangata kaitiaki, researchers and managers dedicated to sustained enhancement of the cultural, economic, social and environmental well being of Māori and New Zealand as a whole through the application of mātauranga and science associated with mahinga kai to modern customary fisheries practices. See www.mahingakai.org.nz for a detailed description of the kaupapa. He Kohinga Rangahau means “the gathering together of research findings”. The report may be used and cited by anyone with due acknowledgement to Te Rūnanga o Ngāi Tahu who are directing and funding the overall project

The Staphylococcus aureus Network Adaptive Platform Trial Protocol: New Tools for an Old Foe (vol 75, pg 2027, 2022)

10.1093/cid/ciac730CLINICAL INFECTIOUS DISEASES75111532-153

Genomic reconstruction of the SARS-CoV-2 epidemic in England

AbstractThe evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021.</jats:p