Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings

Vertical Profile and Temporal Variation of Chlorophyll in Maize Canopy: Quantitative “Crop Vigor” Indicator by Means of Reflectance-Based Techniques

Chlorophyll (Chl) content is among the most important crop biophysical characteristics. Chlorophyll can be related to photosynthetic capacity, thus, productivity, developmental stage, and canopy stresses. The objective of this study was to quantify and characterize the temporal variation of Chl content in the vertical profile of maize (Zea mays L.) canopies by means of a reflectance-based, nondestructive methodology. A recently developed technique that relates leaf reflectance with leaf pigment content has been used for accurate leaf Chl estimation. The technique employs reflectance in two spectral bands: in the red edge (720-730 nm) and in the near infrared (770-800 nm). More than 2,000 maize leaves were measured for reflectance and total and green area during a growing season. A bell-shaped curve showed a very good fit for the vertical distribution of Chl content regardless of crop growth stage. The parameters and coefficients of the bell-shape function were found to be very useful to interpret temporal changes in the vertical profile of each variable. Comparisons among Chl, leaf area index (LAI) and green LAI showed that Chl content was more sensitive to changes in the physiological status of maize than other biophysical characteristics. The quantification of Chl content in canopy should be seen as a useful tool to complement the information on green LAI or LAI. Its applicability will help to improve the understanding of the crop ecophysiology, productivity, the radiation use efficiency and the interplant competition