Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy

Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1–97.7) and mean specificity of 93.3% (CI 90.5–95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance

Artificial intelligence in digital pathology: a diagnostic test accuracy systematic review and meta-analysis

Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology images for diagnostic purposes have rapidly increased in number in recent years. The aim of this work is to provide an overview of the diagnostic accuracy of AI in digital pathology images from all areas of pathology. This systematic review and meta-analysis included diagnostic accuracy studies using any type of artificial intelligence applied to whole slide images (WSIs) in any disease type. The reference standard was diagnosis through histopathological assessment and / or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We identified 2976 studies, of which 100 were included in the review and 48 in the full meta-analysis. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model. 100 studies were identified for inclusion, equating to over 152,000 whole slide images (WSIs) and representing many disease types. Of these, 48 studies were included in the meta-analysis. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4) for AI. There was substantial heterogeneity in study design and all 100 studies identified for inclusion had at least one area at high or unclear risk of bias. This review provides a broad overview of AI performance across applications in whole slide imaging. However, there is huge variability in study design and available performance data, with details around the conduct of the study and make up of the datasets frequently missing. Overall, AI offers good accuracy when applied to WSIs but requires more rigorous evaluation of its performance

Productivity of Soybean under Projected Climate Change in a Semi-Arid Region of West Africa: Sensitivity of Current Production System

The production of soybean is gaining more attention in West Africa. In light of projected changes in climate, there is a need to assess the potential impacts on yield productivity and variability among farmers. An evaluated GROPGRO module of the Decision Support System for Agro-technological Transfer (DSSAT) was used to simulate soybean productivity under both historical (1980–2009) and projected climate scenarios from multiple general circulation models (GCMs) under two representative concentration pathways (RCPs): 4.5 and 8.5. Agronomic data from 90 farms, as well as multiple soil profile data, were also used for the impact assessment. Climate change leads to a reduction (3% to 13.5% across GCMs and RCPs) in the productivity of soybean in Northern Ghana. However, elevated atmospheric carbon dioxide has the potential to offset the negative impact, resulting in increased (14.8% to 31.3% across GCMs and RCPs) productivity. The impact of climate change on yield varied widely amongst farms (with relative standard deviation (RSD) ranging between 17% and 35%) and across years (RSD of between 10% and 15%). Diversity in management practices, as well as differences in soils, explained the heterogeneity in impact among farms. Variability among farms was higher than that among years. The strategic management of cultural practices provides an option to enhance the resilience of soybean productivity among smallholder

Climate Change Impact and Variability on Cereal Productivity among Smallholder Farmers under Future Production Systems in West Africa

Agriculture inWest Africa is constrained by several yield-limiting factors, such as poor soil fertility, erratic rainfall distributions and low input systems. Projected changes in climate, thus, pose a threat since crop production is mainly rain-fed. The impact of climate change and its variation on the productivity of cereals in smallholder settings under future production systems in Navrongo, Ghana and Nioro du Rip, Senegal was assessed in this study. Data on management practices obtained from household surveys and projected agricultural development pathways (through stakeholder engagements), soil data, weather data (historical: 1980–2009 and five General Circulation Models; mid-century time slice 2040–2069 for two Representative Concentration Pathways; 4.5 and 8.5) were used for the impact assessment, employing a crop simulation model. Ensemble maize yield changes under the sustainable agricultural development pathway (SDP) wer

Climate Change Impacts on West African Agriculture: An Integrated Regional Assessment (CIWARA)

The West African Sub-Saharan region (Fig. 1) is home to some 300 million people, with at least 60% engaged in agricultural activity. Climate change is now recognized as a major constraint to development worldwide. While climate change primarily relates to the future, historical trends give evidence of climate change already occurring. Temperature increases of 1 to 1.5◦C have been observed over the last 30 years in West Africa (EPA Ghana, 2001; IPCC, 2007) and there are projections of further warming of the West African region in the foreseeable future (2040–2069; Fig. 2a). The impact of climate change on West African rainfall is less clear. The analysis of historical data over the last 30 years shows that, whereas some zones experienced increased rainfall by as much as 20% to 40%, other locations experienced a decline in annual rainfall by about 15%. Future projections suggest a drier western Sahel (e.g., Senegal) but a wetter eastern Sahel (e.g., Mali, Niger; Fig. 2b). The southern locations of WestAfrica (e.g., Ghana) are projected to experience no change or slight increases in annual rainfall (Hulme et al., 2001). Irrespective of whether these zones will be dryer or not, there is historical evidence of shifts in rainfall patterns with extreme events (i.e., droughts and floods) becoming more frequent (Adiku and Stone, 1995) and it is probable that this trend may persist into the future..

Mobilising adaptive capacity to multiple stressors: insights from small-scale coastal fisheries in the Western Region of Ghana

The processes by which adaptive capacity is mobilised in response to multiple stressors are yet to be fully understood. This study addresses this pressing research gap by drawing on the capitals framework and empirical data from small-scale coastal fisheries in the Western Region of Ghana. It employs an ethnographic approach, based on multiple sources of evidence including documents, interviews and participant observation to examine mechanisms of mobilising adaptive capacity in response to climate and non-climate stressors. Our findings suggest that responding to stressors involves mobilising sets of main-available capitals, such as local innovation, ability to improvise, new technologies, corrupt practices and belief systems (cultural capital); collective action, networks and social ties (social capital); and complaints to the government (political capital). These capitals were the main constituents of adaptive capacity, particularly considering non-responsive government and formal organisations. Further, other forms of capitals, i.e., local leadership, local knowledge, learning capacity, and training (human capital); networks, collective actions, associations and bonding ties (social capital); sand (natural capital); funds from fishing (financial capital), combine in complex ways to mobilise such available capitals. This understanding is critical if synergies among main-available and supporting-available capitals are to support building and mobilizing adaptive capacity. Further, it may help guide important decisions, proactive plans and strategic investment for developing key capitals to enhance adaptive capacity