Survey of liver pathologists to assess attitudes towards digital pathology and artificial intelligence

\ua9 Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ. AIMS: A survey of members of the UK Liver Pathology Group (UKLPG) was conducted, comprising consultant histopathologists from across the UK who report liver specimens and participate in the UK National Liver Pathology External Quality Assurance scheme. The aim of this study was to understand attitudes and priorities of liver pathologists towards digital pathology and artificial intelligence (AI). METHODS: The survey was distributed to all full consultant members of the UKLPG via email. This comprised 50 questions, with 48 multiple choice questions and 2 free-text questions at the end, covering a range of topics and concepts pertaining to the use of digital pathology and AI in liver disease. RESULTS: Forty-two consultant histopathologists completed the survey, representing 36% of fully registered members of the UKLPG (42/116). Questions examining digital pathology showed respondents agreed with the utility of digital pathology for primary diagnosis 83% (34/41), second opinions 90% (37/41), research 85% (35/41) and training and education 95% (39/41). Fatty liver diseases were an area of demand for AI tools with 80% in agreement (33/41), followed by neoplastic liver diseases with 59% in agreement (24/41). Participants were concerned about AI development without pathologist involvement 73% (30/41), however, 63% (26/41) disagreed when asked whether AI would replace pathologists. CONCLUSIONS: This study outlines current interest, priorities for research and concerns around digital pathology and AI for liver pathologists. The majority of UK liver pathologists are in favour of the application of digital pathology and AI in clinical practice, research and education

Survey of liver pathologists to assess attitudes towards digital pathology and artificial intelligence

Aims: A survey of members of the UK Liver Pathology Group (UKLPG) was conducted, comprising consultant histopathologists from across the UK who report liver specimens and participate in the UK National Liver Pathology External Quality Assurance scheme. The aim of this study was to understand attitudes and priorities of liver pathologists towards digital pathology and artificial intelligence (AI). Methods: The survey was distributed to all full consultant members of the UKLPG via email. This comprised 50 questions, with 48 multiple choice questions and 2 free-text questions at the end, covering a range of topics and concepts pertaining to the use of digital pathology and AI in liver disease. Results: Forty-two consultant histopathologists completed the survey, representing 36% of fully registered members of the UKLPG (42/116). Questions examining digital pathology showed respondents agreed with the utility of digital pathology for primary diagnosis 83% (34/41), second opinions 90% (37/41), research 85% (35/41) and training and education 95% (39/41). Fatty liver diseases were an area of demand for AI tools with 80% in agreement (33/41), followed by neoplastic liver diseases with 59% in agreement (24/41). Participants were concerned about AI development without pathologist involvement 73% (30/41), however, 63% (26/41) disagreed when asked whether AI would replace pathologists. Conclusions: This study outlines current interest, priorities for research and concerns around digital pathology and AI for liver pathologists. The majority of UK liver pathologists are in favour of the application of digital pathology and AI in clinical practice, research and education

Evaluation of satellite evapotranspiration estimates using ground-meteorological data available for the Flumen District into the Ebro Valley of N.E. Spain

The definitive version is available at: http://www.sciencedirect.com/science/journal/03783774Accurate estimation of actual evapotranspiration (ETa) is essential for effective local or regional water management. At a local scale, ET estimates can be made accurately considering a soil–plant-atmospheric system, if adequate meteorological-ground data or ET measurements are available. However, at a regional scale, ETa values cannot be measured directly and the estimates are frequently subject to errors. Although it is possible to extrapolate to the regional scale from local (point) data of meteorological stations, the relative sparse coverage of ground estimate can make this problematic without some spatial analysis to demonstrate the similarity of the climate in the area. The introduction of remote sensing data and techniques offers alternatives both to estimate variables (i.e. radiation) and parameters (i.e. ET) with few spatial restrictions, thus, it provides potential advantages to the regional ETa computation. In particular, the use of remote sensing procedures like the surface energy balance-based algorithms (SEB) have been successfully applied in different climates, enabling the estimation of ETa at local and regional scales. A proper variation of the Surface Energy Balance Algorithm for Land (SEBAL) was applied to 4 years of data for the Flumen District in the Ebro Basin at the N.E. of Spain. Results obtained show that the remote sensing algorithm can provide accurate daily ETa estimations as compared with lysimeter measurements of daily ET values for two crop plots: one with a reference grass and other with maize or wheat as function of the season. Also a comparison between ETa and the reference and crop ET values applying the Penman–Monteith method was carried out. The comparison analysis consider an accepted error difference of 1.0 mm d−1 (20% of error) for local scale, the ETa values for the grass show a bias of 0.30 mm d−1 against the ETgrass and a bias of 0.36 mm d−1 against ETo. Differences between ETmaize or ETwheat and ETa against their average showed an acceptable agreement for the field with sdiff ± 0.6 mm d−1. For the crop fields at regional scale external causes associated to atmospheric and surface variations (i.e. land preparation) rather to the remote sensing algorithm made difficult to determine a percentage of error. Finally, ETa values were obtained at regional scale and it was demonstrated that using the remote sensing improve significantly the crop ET estimations computed in the area using traditional methods.The authors gratefully acknowledge the financial support of DGAPA-UNAM and CONACyT, Mexico.Peer reviewe

Crop management in a district within the Ebro River Basin using remote sensing techniques to estimate and map irrigation volumes

13 Pags.- 1 Tabl.- 6 Figs. This paper was presented at the River Basin Management 2005 conference in Bologna, Italy.An assessment of water management in the Flumen District, Central Valley of the Ebro River Basin in Spain, using the remote sensing technique Surface Energy Balance Algorithm for Land (SEBAL) was performed. This assessment was based on the estimation of the actual ET (ETa) to compute net water volumes (Vn). This work extended the analysis by also computing net irrigation volumes (Vi) by introducing a water application efficiency as a function of morphopedologic units (Eam). Two approaches were adopted for SEBAL Vi: a) the crop water demands including months outside the crop season (SEBAL_F1) and b) the crop water demands of the six main crops only in the growing season (SEBAL_F2). The comparison analyses for SEBAL_F1 and SEBAL_F2 Vi show a very good agreement with a bias of 0.09 hm3 and 0.56 hm3, respectively. As a result of an accurate estimation of Vi, the water use efficiency (Ea) for the whole Flumen District was determined to be from 80% to 90%. These are actual figures, thus it is possible to review the current crop and water management, identifying the possible causes for low irrigation efficiency on some plots.The authors would like to thank Dr. Allen and Dr. Bastiaanssen for their support during the study. The authors gratefully acknowledge the financial support of DGAPA-UNAM and CONACyT, Mexico.Peer reviewe

Crop management in a district within the Ebro River Basin using remote sensing techniques to estimate and map irrigation volumes

13 Pags.- 1 Tabl.- 6 Figs. This paper was presented at the River Basin Management 2005 conference in Bologna, Italy.An assessment of water management in the Flumen District, Central Valley of the Ebro River Basin in Spain, using the remote sensing technique Surface Energy Balance Algorithm for Land (SEBAL) was performed. This assessment was based on the estimation of the actual ET (ETa) to compute net water volumes (Vn). This work extended the analysis by also computing net irrigation volumes (Vi) by introducing a water application efficiency as a function of morphopedologic units (Eam). Two approaches were adopted for SEBAL Vi: a) the crop water demands including months outside the crop season (SEBAL_F1) and b) the crop water demands of the six main crops only in the growing season (SEBAL_F2). The comparison analyses for SEBAL_F1 and SEBAL_F2 Vi show a very good agreement with a bias of 0.09 hm3 and 0.56 hm3, respectively. As a result of an accurate estimation of Vi, the water use efficiency (Ea) for the whole Flumen District was determined to be from 80% to 90%. These are actual figures, thus it is possible to review the current crop and water management, identifying the possible causes for low irrigation efficiency on some plots.The authors would like to thank Dr. Allen and Dr. Bastiaanssen for their support during the study. The authors gratefully acknowledge the financial support of DGAPA-UNAM and CONACyT, Mexico.Peer reviewe