There has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation.MEDLINE and EMBASE were searched for AI CMR segmentation studies in April 2022. Any fully automated AI method for segmentation of cardiac chambers, myocardium or scar on CMR was considered for inclusion. For each study, compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was assessed. The CLAIM criteria were grouped into study, dataset, model and performance description domains.209 studies published between 2012 and 2022 were included in the analysis. Studies were mainly published in technical journals (58%), with the majority (57%) published since 2019. Studies were from 37 different countries, with most from China (26%), the United States (18%) and the United Kingdom (11%). Short axis CMR images were most frequently used (70%), with the left ventricle the most commonly segmented cardiac structure (49%). Median compliance of studies with CLAIM was 67% (IQR 59–73%). Median compliance was highest for the model description domain  and lower for the study , dataset  and performance  description domains.This systematic review highlights important gaps in the literature of CMR studies using AI. We identified key items missing—most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis—that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence to established frameworks for reporting standards and presents recommendations for improving the quality of reporting in this field.www.crd.york.ac.uk/prospero/], identifier [CRD42022279214].[ Cardiac MRI (CMR) is the gold standard for non-invasive assessment of cardiac structures. Quantitative measurement of cardiac volumes can be achieved with CMR and relies on accurate segmentation of structures on CMR images. Manual segmentation is routinely performed by cardiac imaging experts but suffers from a number of drawbacks. In addition to being laborious and time-intensive, manual segmentation is operator-dependent, potentially impacting interobserver agreement. As the demand for cardiac imaging continues to grow and outpaces the supply of trained readers, there is an increasing need for automation , 2.Artificial intelligence (AI) is changing medical imaging through the automation of complex and repetitive tasks, including the segmentation of anatomical structures . MachineA growing number of studies have reported the use of AI methods for segmentation in CMR. The manner in which these studies are reported is important. Transparent reporting of methods and results facilitates reproducibility and allows proper evaluation of validity. Equally, a consistent standard of reporting aids comparison between studies and may improve accessibility of the literature, which may be of particular benefit in a rapidly expanding field such as AI. The need for consistency in reporting medical research is well recognized and reflected in various guidelines and checklists for different study types. The Checklist for Artificial Intelligence in Medical Imaging (CLAIM),  has adopThe study protocol was registered with The International Prospective Register of Systematic Reviews . The study was undertaken and is presented in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines . No ethiex vivo images, and conference publications. Studies reporting the use of AI for segmentation of structures in CMR were considered for inclusion. Studies were deemed eligible if they reported: (1) any type of fully automated AI method , (2) segmentation of cardiac chambers, myocardium or scar tissue and (3) use of adult human CMR images, regardless of acquisition methods (such as use of intravenous contrast), parameters, post-processing methods and software. Exclusion criteria were as follows: absence of a newly developed segmentation model , use of semi-automated AI methods , multiorgan segmentation, combined segmentation of multiple imaging modalities , segmentation of cardiac vessels  or pericardial tissue, use of non-human or The MEDLINE and EMBASE databases were searched for relevant studies on April 20 2022. The search strategy is outlined in the Three authors extracted data from the included studies  according to a standardized checklist. Half of the included studies were also evaluated independently by an additional five authors  for the purpose of quality control. All discrepancies were resolved with discussion, with SA acting as an arbitrator, and the final extracted data confirmed. Descriptive information about each study was recorded, including publication details , data used  and AI model . The studies were assessed for compliance against the 42 criteria of CLAIM, which were grouped into four domains: study description (9 criteria), dataset description (17 criteria), model description (6 criteria) and model performance (10 criteria). The database search yielded 2,855 hits from which the title and abstract screening identified 364 relevant studies. The subsequent full-text assessment deemed 209 eligible for inclusion in the analysis .Descriptive information for all of the 209 included studies are provided in Publicly available datasets were used in 49% of studies, and single or multicenter non-public datasets used in 61%, 17% of studies used multiple or combined datasets (including multiple public datasets and a combination of public and non-public datasets). A minority of studies (6%) did not report their data source . Of the Model validation was mostly reported using internal holdout methods (78%), such as cross-validation. A minority reported testing on external and mainly public datasets 22%, . The DicResults for compliance with the domains and selected individual criteria of CLAIM are summarized in Median compliance with the study description domain was 71% (IQR 63–86%). Almost all studies clearly indicated the use of AI methods (91%) and their objectives (94%). Where non-public datasets were used, only a minority of studies (36%) indicated whether these were prospective or retrospective. No studies provided access to a full study protocol. Sources of funding were declared in 82% of studies.Median compliance with the dataset description domain was 63% (IQR 50–67%), the lowest of the four domains. The source of the dataset was reported in most studies (94%). While most studies provided eligibility criteria for included cases (74%), few studies reported their demographic and clinical characteristics (18%) or indicated the flow of these cases (10%) in sufficient detail. Details regarding the calculation of the intended sample size (4%) and how missing data were handled (9%) were also infrequently reported. The definition of the ground truth reference standard was provided in 68% of studies. Where non-public datasets were used, the source of ground truth annotations and annotation tools were stated in 55% and 31% of studies respectively, with inter- and intra-rater variability reported in 42%. The majority of studies reported data preprocessing steps (94%), definitions of data elements (99.5%), how data were assigned to partitions (89%) and the level at which partitions were disjoint (87%).Median compliance with the model description domain was 100% (IQR 80–100%), the highest of the four domains. The majority of studies provided details about the model used (95%), initialization of model parameters (92%), training approach (78%) and method of selecting the final model (92%). The software libraries, frameworks and packages used were reported in 74%.Median compliance with the performance description domain was 60% (IQR 50–70%). A minority of studies reported testing on external data (22%) Almost all studies provided metrics of model performance (99.5%). Most studies provided statistical measures of significance and uncertainty when reporting results (78%). Many studies provided forms of robustness or sensitivity analysis (61%) and methods for explainability and interpretability (64%). A minority of studies reported failure analysis for incorrectly classified cases (32%). Most studies discussed their limitations (76%) and implications for practice (76%).Poor reporting is a major source of research waste , 11 and Accurate and sufficiently detailed descriptions of study materials and methods are of particular importance for AI studies in medical imaging to allow the assessment of reproducibility and reliability of results. Overall compliance with CLAIM was highest for the model description domain, with most studies providing a description of the model and details of training approaches. However, this was lowest for the dataset description domain, which indicated variable reporting of the data sources used to train and evaluate models.A good understanding of data sources is a prerequisite for evaluating the validity of AI models. Although most studies identified their data sources, this was a significant omission in the studies that did not and one which greatly limits their interpretability. Public datasets were used in almost half of the studies, with the majority of these made available through segmentation challenges hosted by MICCAI . Public Detailed description of model training and performance is expected in this field. Testing model performance on external data was performed in less than a quarter of all studies. Model generalizability can only be fully evaluated when performance is assessed in demographic and clinical populations different from the original training cohort. The reported external datasets were small and captured only limited variations in imaging appearances. This represents a major hurdle to overcome before AI models can be implemented in clinical practice. We also noted subjectively that many publications used the terms “validation” and “test” interchangeably, or failed to distinguish these methods clearly. Regarding the use of data in AI studies, a validation set is used to optimize hyperparameters and performance between training epochs, while a testing set is used to assess the performance of the final model. The lack of consistent terminology in studies can limit the interpretability of their models and blur the distinction between internal holdout and external testing methods. Additionally, few studies reported failure analysis of incorrectly classified cases, suggesting that most did not explore the reasons for model underperformance. Furthermore, the vast majority of studies did not discuss the limitations of their methods, limiting their transparency. Open publishing of source code is a contentious topic in AI research and was only provided in one in ten of all studies. The public availability of code aids transparency, assists peer review and facilitates the development of new models, but bears important implications for ownership and rights.The use of reporting frameworks, such as CLAIM, can be beneficial. For example, they may help to inform study design and highlight areas that may require rectification prior to dissemination of results. Frameworks assist standardization in reporting, improving comparability and interpretability by the wider scientific community. Study accessibility is also an important consideration in advancing the field. Regardless of journal type, AI studies in medical imaging need to cater for a broad potential readership, from clinicians to computer scientists. More standardized reporting and the use of consistent and accessible terminology are important in this regard.arxiv.org search reveals numerous segmentation attempts of varying levels of reporting quality and beyond the scope of this review to capture. Thirdly, even despite the use of structured tools such as CLAIM, there remains an element of subjectivity in determining report quality, such as the amount of information required for a study to be deemed reproducible.We acknowledge limitations in this systematic review. Firstly, this review focused solely on AI segmentation in CMR studies. However, these findings are likely to apply to AI studies in other cardiac imaging modalities, such as echocardiogram, CT coronary angiography or nuclear myocardial perfusion studies. Furthermore, given that AI studies in chest imaging have shown similar shortcomings in reporting quality , our finThis systematic review highlights the variability in reporting and identifies gaps in the existing literature of studies using AI segmentation of CMR images. We identified several key items that are missing in publications—most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis—which may limit study transparency, reproducibility and validity. This review supports closer adherence to established frameworks for reporting standards, such as CLAIM. In light of these findings, we have presented a number of recommendations for improving the quality of reporting of AI studies in both CMR and the wider field of cardiac imaging.The original contributions presented in this study are included in the article/SA and AS conceived the idea and need for the systematic review and contributed to the study conception and design. SD and SA performed the protocol registration in PROSPERO. SA created the search strategy and performed the literature search. SA, SD, MaS, MiS, and AqM performed screening and eligibility assessments independently. SA, SD, AhM, AqM, MaS, MiS, SJ, MG, VR, and HA evaluated the included studies and collected relevant data. SA, AhM, MaS, AqM, and SJ performed the material preparation and analysis. SA, AhM, SJ, and MaS drafted the manuscript, figures, and tables. SA, AhM, AqM, VR, and SD wrote the first draft of the manuscript. SA and AhM wrote the final draft, taking into account comments and suggestions from experts in the field AS, DO’R, HL, RG, MM, and PG. All authors contributed to the interpretation of data, commented on previous versions of the manuscript, read and approved the final manuscript, took part in the critical review and drafting of the manuscript, and have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


While some academic health centers have organizational structures to support educational scholarship, such as Medical Education Research Units (MERU), many education scholars may lack access to such institutional resources to support their research agenda and professional growth.In 2014, as part of an externally funded education grant, three faculty educators established a unique education data management system Research & Evaluation Data for Educational Improvement (REDEI). Lacking an institutional MERU, they established an educational research community of practice (CoP) using REDEI as the research dataset. The senior faculty member’s effort to facilitate the group is funded by the Dean’s Office.The CoP meets every 2 weeks to generate research ideas, discuss analytic approach and strategy, review analyses designed to address or explore a research question, and plans for manuscript development. Our CoP has grown from 3 to 18 members representing faculty educators, administrators, and staff across many departments in the School of Medicine. As of 2021, the REDEI system contains performance data on 1,246 students across all years of undergraduate medical education. To date, we have published 11 peer-reviewed educational research manuscripts. Five learners have served as coauthors , three of whom were first authors. Eleven additional papers are in process. This community of practice supports productivity, provides mentorship, overcomes barriers, and is flexible enough for people to join when they can or when an area of interest is actively under development.We are working on educational interdisciplinary research grant submission and creating collaborations with other institutions. Our focus remains on honing skills in grantsmanship, identification of impactful research questions, application of rigorous methods and instrumentation to address them, and refining process of budget development, timelines, and other planning strategies. Participating in the scholarship of medical education enables educators to learn from each other, build on their achievements, and career advancement. While many medical education professionals derive deep and intrinsic rewards from their scholarly work, those who must also publish for promotion and tenure may also face external pressures to pursue a high level of scholastic output without the support that researchers in clinical medicine or basic sciences often have. Regardless of motivation for performing medical education scholarship, scholars in medical education face a myriad of obstacles to successfully advance educational research. These include, but are not limited to: a lack of training in educational research methods, design, and statistical analyses; lack of appreciation for educational rigor and scholarship; limited opportunities for extramural funding; time demands due to competing responsibilities and lack of protected time; and a need for mentorship, community, accountability, and rewards .Varying organizational structures have been developed to support the academic mission of educational researchers, such as the creation of formal offices or departments of medical education or medical education research units (MERU) . In the Originally proposed in 1991 by Lave and Wenger, communities of practice (CoP) represents a conceptual framework that describes a group of ‘people who share a concern or a passion for something they do and learn how to do it better as they interact regularly’ . Over tiResearch & Evaluation Data for Educational Improvement (REDEI) system houses student performance on a variety of formative and summative educational metrics. REDEI’s dashboards include performance on weekly quizzes, end of block examinations  [We received funding to transform OHSU’s undergraduate medical education (UME) curriculum from a time-based to time-variable competency-based program from the AMA . The supe exams) , as welle exams) . ScholarWhen REDEI was initially developed, we applied for and received Institutional Review Board (IRB) and Family Education Rights and Privacy Act (FERPA) Approvals for use of REDEI Data for research purposes  and addiREDEI User Scholarship Hopefuls (RUSH) team.Development of the REDEI system involved a senior faculty member and educational researcher (Author PAC) team leader, a web developer, and an assistant dean for student affairs. This team, known as the REDEI team, met every other week beginning in 2014 to develop, review, and revise REDEI features as ongoing needs were identified. By 2016, the REDEI Data system had captured performance data on 453 medical students across a variety of measures, and data were stable enough to be used for educational research. As the structural and operational features of the portfolio system matured , the REDet al., as discussed in “The Problem” section [The RUSH team made this transformation to address the institution’s desire to develop educators interested in learning educational research and academic writing skills. Accordingly, we intentionally developed RUSH as an educator’s CoP using the definition espoused by McGrath  section . With thCoPs aim to foster learning and growth via peer mentorship and shared practice among a group of people who share a common passion . To creaThe initial core REDEI group of 3 grew to 9 members in 2016, to now 18 core members of the RUSH team who attend bi-weekly meetings when possible . RUSH haResearch Jeopardy game where participants choose correct findings among incorrect ones from recently published educational research projects. The game is designed to help members recognize the often-wrong assumptions commonly made about educational research outcomes and to support critical thinking and assessment of research rigor. The remainder of the time is typically spent identifying and discussing potential projects, reviewing data/making decisions on data quality , discussing the results of analyzed data, and identifying and planning next steps for manuscript development. We use a research idea/manuscript tracking system to manage progress. Participants sign up in the tracking system to work on a project of interest, which includes manuscript title, lead and senior authors, writing group, date of data maturity, target journal, and submission timeline. The tracking database provides the entire group with a shared mental model from which to work, starting with the research idea and following through to manuscript development phases, submission status, and revising/resubmission management. The tracking database helps the CoP maintain a shared vision.RUSH team meetings begin with a all four criteria: (1) substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; (2) drafting the work or revising it critically for important intellectual content; (3) final approval of the version to be published; and (4) agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved [We use the International Committee of Medical Journal Editors’ (ICMJE) four-point authorship criteria to guide author responsibilities, where all authors must meet resolved .After projects and team members are identified, and next steps delineated, team members work on manuscript development using a two-group approach: Group 1 produces the first rough draft while Group 2 undertakes critical revisions. We have found it helpful to save fresh eyes for Group 2 work, as it serves as a ‘mini review.’ We aim for no more than four drafts prior to submission, and the objective for each review is clearly conveyed prior to work on the respective draft. We have also found that everyone gains valuable experience by taking turns on each writing group, according to their interest and available bandwidth during the production of the manuscript. If someone signs up for an authorship role but is unable to fulfill the four responsibilities, the lead and senior authors remind them of the criteria and ask if they feel they have met them. Thus far, this approach has not changed the dynamic of the CoP, and the authors who have not met criteria have either stepped up to meet them or have agreed to be removed from the paper.The educational researcher team lead, a senior faculty member who devotes 0.20 FTE to the effort, is funded by the Dean’s Office. This a funding mechanism which bypasses more traditional departmental or office funding models. This individual (1) manages the agenda, develops Research Jeopardy questions, and facilitates RUSH meetings; (2) oversees and conducts data hygiene activities; (3) conducts data analyses; (4) develops the research methods and results sections of manuscripts; (5) provides mentoring on manuscript development and publishing ethics; and (6) assists with grant development and submissions to advance the educational research effort. The Web-designer/programmer who works on data capture and structures and creates analytic files dedicates 1.0 FTE.RUSH participants receive mentoring in educational research that is often not available in their departments due to a lack of expertise or competing demands. As part of this mentoring, their skills in manuscript development, revision, and resubmission improve and they make meaningful progress in their scholastic mission. Similarly, they receive assistance with educational grant development, submission, and, if funded, project management that further advances their skills as educational researchers. The collaborative ideas and newly formed partnerships across departments and divisions have been instrumental in contributing to a highly functional CoP.Since 2016, members of our CoP have published 11 peer-reviewed educational research manuscripts, with an additional one currently in review . Five leAs the sophistication of our CoP has grown, we have begun collaborating on educational research grant submissions, many of which are multi-institutional and interdisciplinary. This provides additional skills in grantsmanship, including networking and relationship development, to advance educational research beyond a single setting. In addition to fostering advancement of junior faculty, success in grant attainment provides protected time and other resources to conduct studies that are currently added on to busy schedules. To conduct more impactful research than we are currently able to do at our own institution, we have also requested datasets from the AAMC to conduct larger studies, such as identifying predictors of success among students who are underrepresented in medicine. Finally, we are exploring how to leverage our CoP process and shared expertise to support projects outside of our original focus of REDEI platform research questions. We have found that the CoP approach has fostered educational scholarship effectively at our institution and hope that other academic institutions in medicine may benefit from the community of practice model we have implemented, as it may be generalizable to other settings. The Health Professions Education Scholarship Units (HPESU), another possibility, would require more institutional support than is currently available at OHSU.


Integrated health systems with strong laboratory networks are critical in improving public health. The current study assessed the laboratory network in Ghana and its functionality using the Assessment Tool for Laboratory Services (ATLAS).A national-level laboratory network survey was conducted among stakeholders of the Ghanaian laboratory network in Accra. Face-to-face interviews were conducted from December 2019 to January 2020, with follow-up phone interviews between June and July 2020. Also, we reviewed supporting documents provided by stakeholders for supplementary information and transcribed these to identify themes. Where possible, we completed the Laboratory Network scorecard using data obtained from the ATLAS.The Laboratory Network (LABNET) scorecard assessment was a valuable addition to the ATLAS survey as it quantified the functionality of the laboratory network and its overall advancement toward achieving International Health Regulations (2005) and Global Health Security Agenda targets. Two significant challenges indicated by respondents were laboratory financing and delayed implementation of the Ghana National Health Laboratory Policy.Stakeholders recommended a review of the country’s funding landscape, such as funding laboratory services from the country’s internally generated funds. Also, they recommended laboratory policy implementation to ensure adequate laboratory workforce and standards. The importance of laboratory outbreak response has been highlighted by the severe acute respiratory syndrome , H1N1 (2009), meningitis (2009), cholera (2010), Middle East respiratory syndrome (2012), Ebola virus disease (2014), Zika virus disease (2015), and yellow fever (2016–2017) outbreaks, as well as the current coronavirus disease 2019 (COVID-19) pandemic.9 There are considerable delays in outbreak detection and communication for most infectious disease outbreaks originating from Africa,11 as exemplified in the delayed response to the West African Ebola outbreak, which was reported four months after the index infection and, more recently, the delayed diagnosis of suspected COVID-19 cases in Africa.13 Also, the COVID-19 pandemic has underscored the diagnostic capacity challenges facing African countries. As of January 2022, Ghanaian SARS coronavirus 2 (SARS-CoV-2) daily testing rates was around 70 per 1000 people, in contrast to over 2300 per 1000 people in the United States.14Integrated health systems with effective and efficient laboratory networks15 The Global Health Security Agenda (GHSA) requires countries to establish tiered national laboratory systems and ‘determine an appropriate level of diagnostic capability at each level of the public health hierarchy from national to the district’.16Improving laboratory networks is a critical step toward controlling health emergencies.17 The assessment identified the need for guidelines in biosafety, inventory control and logistics management information systems, and the application of policies and procedures.18 However, there have been no publicly available assessments of the laboratory network in Ghana since then. In this study, we conducted the ATLAS survey with a wider scope, including a Laboratory Network (LABNET) scorecard evaluation.19The laboratory network in Ghana was assessed in 2006, focusing on HIV diagnostic resources, using the Assessment Tool for Laboratory Services (ATLAS).The ATLAS survey was conducted using the central-level questionnaire to describe the national laboratory network in terms of its organisational structure, management of laboratory services and logistics, as well as quality regulation. A portion of the LABNET evaluation was added to provide a quantitative measure of functionality to corroborate the qualitative findings from the ATLAS survey on core capabilities. The assessment was conducted to identify strengths and weaknesses of the laboratory network and provide recommendations for its improvement.The study obtained ethical approval from the Ghana Health Service Ethical Review Committee (GHS-ERC 005/05/19) and Noguchi Memorial Institute for Medical Research Institutional Review Board (FWA 00001824). In addition, the Director-General of the Ghana Health Service (GHS) granted permission before the study was conducted. Written consent was obtained from every participant before conducting an interview.This was a qualitative study conducted among key stakeholders in the laboratory network in Accra, Ghana. It is part of a more extensive, ongoing study to map and model the laboratory network in Ghana for three epidemic-prone diseases (EPDs) – bacterial meningitis, measles and yellow fever – and three diseases of public health importance (DPHI): HIV, tuberculosis and hepatitis C virus (HCV). These diseases guided our choice of stakeholder interviewed.20 to suit the Ghanaian context, as recommended in the ATLAS guidelines. We identified stakeholders to be interviewed with the ATLAS during the consultation session. The ATLAS comprises nine sections: organisation, policy, forecasting and procurement, financing, storage and distribution, inventory control system, laboratory information management system, supervision, and general questions.We had a consultation with stakeholders from the National Public Health & Reference Laboratory (NPHRL) and Disease Surveillance Department of GHS in November 2019 to adapt the national-level United States Agency for International Development ATLASFace-to-face interviews were conducted from December 2019 to January 2020, with follow-up phone interviews conducted between June 2020 and July 2020. Also, supporting documents provided by stakeholders were reviewed for supplementary information, and responses were transcribed to identify themes.19 The LABNET scorecard has been validated for assessing a country’s laboratory network functionality in implementing the International Health Regulations (2005) and attaining the GHSA targets.19 Our LABNET scorecard assessment was based on five of the nine core capabilities closely related to aspects of the laboratory network assessed by the ATLAS: political, legal, regulatory and financial framework; structure and organisation of the laboratory networks; laboratory information (management system); infrastructure, equipment and supplies; and quality of laboratory services. The scores were converted to percentages reflecting overall advancement toward standards or targets, and interpretations were drawn on each component’s functionality stage based on the weakest score (Supplementary Table 1).Where possible, data were used to complete the LABNET scorecard.A total of 12 respondents were interviewed: two representatives from Clinical Laboratories Unit (CLU) under the Institutional Care Division, GHS; Deputy Directors at Supplies, Stores and Drugs Management (SSDM) and the Finance Division, GHS; the Head and Quality Manager for NPHRL; and a representative each from the Health Facilities Regulatory Agency and Allied Health Professionals Council. Relevant stakeholders from disease control programmes including the Expanded Programme of Immunisation (EPI) with focus on EPDs, National Tuberculosis Control Programme (NTP), National AIDS and Sexually Transmitted Infections Control Programme (NACP) and National Viral Hepatitis Control Programme (NVHCP) were also interviewed.n = 342), haematology (n = 397) and microbiology tests (n = 365). The Public Health Division of the GHS is responsible for the Public Health Laboratories (PHLs) that surveil measles, rubella, yellow fever, tuberculosis, HIV and other diseases. The NPHRL in Accra has oversight responsibility for three other PHLs situated in Tamale (Northern zone), Kumasi (Middle zone), and Sekondi (Western zone).The GHS tiers into the national, regional, district and sub-district levels with the CLU coordinating vertical laboratory activities . RespondLaboratories in the GHS organisational structure are tiered into three levels: tertiary , secondary  and primary laboratories . The relationship between tiers is based on referrals, flowing from lower-tier to higher-tier laboratories, with higher-tier facilities assigned some oversight responsibilities on lower-tier laboratories . The four teaching hospitals, although directly under Ministry of Health (MoH) but not GHS, only play a key role in the referral system. The regional laboratory scientists also serve as laboratory coordinators in various regions. However, the districts lack such coordinators due to inadequate staff capacity.The EPI, NACP, NTP and NVHCP collaborate with clinical laboratories and PHLs to conduct laboratory testing. The EPI has outsourced all laboratory services and logistics management for EPDs to NPHRL. While the NACP has ART sites at 488 facilities, including laboratories, the NTP works with about 337 laboratories in the health system. The NPHRL coordinates national-level activities of NVHCP, whereas the health directorates coordinate activities at the regional and district levels. There are no specific laboratories designated for HCV testing under the current programme.21 that was approved in 2013; however, the Ghana National Health Laboratory Policy is still not operational. The policy describes the laboratory organisation, services and test menu by level, staffing norms, logistics management, quality management and laboratory information system. The primary level test menu covers basic parasitology and bacteriology, cytology, histopathology, serology, clinical chemistry and haematology tests. In contrast, the secondary level test menu covers more sophisticated testing such as immunohistochemistry and nucleic acid testing. Finally, the tertiary level test menu covers more specialised tests and the PHLs focus on EPD-DPHIs in support of public health surveillance . Also, the clinical laboratories conform to international and funding programme standards. However, harmonised general clinical testing is lacking, particularly the use of different automated equipment at the facility level for haematology, immunology and chemistry.The policy document on infection prevention and control23Funding for laboratory services in the country is fragmented for infrastructure, supplies and equipment. There are different funding sources for target programmes and PHLs; general clinical testing relies on internally generated funds and National Health Insurance System (NHIS) reimbursement on diagnostic services and tests listed on the NHIS benefits package.The government of Ghana only provides funding for infrastructure, some equipment and workers’ salaries. The primary funding donors were the Global Fund , which p24; hence, there is no specific budget line for laboratory services. Since Ghana attained lower-middle-income status, a transition plan was implemented in 2015, requiring the government to increase funding each year with a full transition scheduled in 2022.Different units and divisions coordinate programme-specific testing for EPD-DPHIs. Most programmes procure laboratory supplies at the central level rather than allocate funds to laboratories. The NPHRL receives most supplies and funds from donors, allocating these to the zonal PHLs through their divisional heads. Outbreaks also determine the allocation of financial resources. Target programmes and the NPHRL have separate budgetary line items for laboratory services, supplies and equipment. However, the finance unit of the GHS works with a highly aggregated budget, the Medium Term Expenditure Framework plan for MoHThe target programmes, NPHRL and health facilities consistently forecast and procure needed laboratory supplies; however, there is no standardised forecasting method. The EPI does not prepare forecasts for EPDs because they have outsourced this role to NPHRL. The situation is quite peculiar for NVHCP as they also prepare forecasts but do not receive funding for laboratory supplies.Public Procurement Act 663 guides procurement as amended in 2016. For example, at NTP, a procurement request to SSDM initiates an open tender with the selection of vendors by the Central Tender Committee. A diverse stakeholder team monitors the procurement process. The average lead time is programme-specific, being six months for NACP and inconsistent for NTP due to time lags between contract award and item supply. Although the programmes operate independently, there are instances when they carry out joint activities, especially between the NTP and NACP. Their procurement units lead procurement and monitoring for general clinical testing of their target disease conditions at the facilities.Unless otherwise requested by funders, the All programmes indicated adequate laboratory supplies, except the NVHCP, which has no funding for laboratories. The NACP supplies reagents and haematology and chemistry analysers to the ART sites, whereas the NTP supplies reagents to all tuberculosis testing laboratories. However, there was little information on the adequacy of supplies at the PHLs and clinical laboratories, as this assessment was conducted only at the national level. Although multiple systems manage laboratory supplies, respondents reported that duplicating efforts had been minimised.The Integrated Scheduled Delivery System implements the Last Mile Distribution Strategy of laboratory supplies and equipment to all levels other than PHLs. Currently, there are four storage facilities for laboratory supplies and cold chain reagents at the central level, and although these storage facilities have adequate cold chain capacity, the regional stores do not. Thus, a central medical store is being built for future storage needs. A third-party logistics firm is contracted for distribution as GHS delivery vehicles are insufficient. The NPHRL handles supplies at the national level for onward distribution to the zonal PHLs. The programmes intervene when there is a need for redistribution or emergency distribution.The International Organization for Standardization (ISO) 15189 requires establishing an inventory system with preset minimum and maximum stock levels. Hence, SOPs have been developed by the SSDM indicating the minimum, reorder points and maximum stock levels for facilities. The minimum and maximum stock levels are two and three months for the central level and three and six months for the regional medical stores. The laboratory facilities determine order quantities for general clinical testing supplies, higher-level authorities at the central level for programmes, and the head of NPHRL for all PHLs. The laboratory scientists at the facilities collaborate with their procurement units to conduct stock-taking twice annually, using various mechanisms, such as WhatsApp platforms , Microsoft Excel spreadsheets , and Gx-Alerts , aside from the monthly stocks reports. The WHO monitors stock for measles, rubella and yellow fever through kit management reports regularly submitted by the NPHRL, while the NPHRL monitors activities, including stock balances, at the zonal PHLs.® machines . Thus, the available 127 GeneXperts® are strategically placed within a 2-h drive to most facilities, easing specimen referral. The Ghana Postal Courier Service is contracted to transport samples on specific pickup days, ensuring collection within two days. Tuberculosis drug resistance testing is performed at five laboratories, with specimen referral arranged by the requesting facility through public transport or courier service. HIV viral load testing and early infant diagnosis are performed at 10 regional and four teaching hospitals. As with tuberculosis diagnosis, the courier service transports HIV specimens on facility-specific pickup days. The requesting facility arranges specimen transport for all other specimen referrals.Based on the current testing algorithm, sputum samples from all new suspected cases are tested using GeneXpertThe health information management system used in all health facilities is the DHIMS2. This database is an upgrade from the DHIMS, which incorporates tracking by target programmes and improves cause-of-death statistics. Like most clinical databases, the DHIMS2 captures laboratory service data at an aggregate level down to facility, but not patient level. The basic laboratory information system also reports into DHIMS2 periodically. Similarly, e-tracker data for HIV viral load are captured in the DHIMS2. Two other databases do not directly report data into DHIMS2: Gx-Alert for GeneXpert, and EpiInfo  or data on EPDs. Gx-Alert data are aggregated at the facility level and entered manually into DHIMS2 whereas EpiInfo captures data on the WHO disease network that are reported directly to the WHO. No entries are made into DHIMS2 .The PHLs prepare weekly yellow fever reports and communicate positive results via phone. Other reports are submitted either electronically or as hard copies or by both methods. Standard national forms used to collect and report data into DHIMS2 include case-based forms and other national forms developed by the CLU for haematology, chemistry and microbiology testing. They also provide demographic data crucial for planning immunisation activities. There are also logbooks at the laboratories that provide information on the laboratory tests requested and conducted. However, only aggregated data are available at the national level.As described, reports from these databases are useful for quantifying commodities during forecasting, procurement, inventory control, targeted screening, planning public health interventions, notifying international organisations on disease burden and outbreaks, and seeking donor support. However, not all facilities submit data as and when due. The NPHRL monitors reporting rates and supports supervision exercises organised by PHLs.The CLU does not supervise private laboratories. Instead, it carries out supportive supervision for general clinical testing at the regional level. Another regional-level team supervises the district and sub-district levels. In addition, the CLU performs supervisory visits every six months using the OTSS checklist for malaria diagnosis and the Integrated Monitoring and Supervision Checklist for general supervision.For the PHLs, a national-level supervisory team monitors reagent availability, storage and shelf life, and staff competencies and credentials using checklists. Supervision is quarterly at the NPHRL but less frequent at the zonal PHLs due to insufficient logistics; however, the laboratory managers monitor daily activities.Target programmes, mainly NACP and NTP, organise supervisory visits to laboratories every quarter or twice annually, depending on resources. These programmes are building local capacity so that representatives can supervise activities at the district level. The EPI only provides supervision and support for the national and regional cold rooms.25 The CLU also carries out external quality assurance through OTSS  or 90%  of items. Unannounced laboratory monitoring is carried out twice annually and could lead to a reassignment of the tier, influencing the laboratory’s NHIS reimbursement.The Allied Health Professionals Council regulates laboratory professionals through licensing examinations and sanctions. Although the Council is enjoined by Act 857 to supervise practitioners, this is lacking due to fund constraints. Instead, their interventions are responses to malpractice brought to their attention.The self-report of stakeholders on five of the nine core capabilities of the laboratory network using the LABNET scorecard indicated that the laboratory information management system advanced the most (74%) toward achieving the International Health Regulations (IHR) and GHSA targets  and GHSA.Based on the experiences described, we drew lessons, and identified some strengths and challenges:The ATLAS was a useful baseline assessment of the entire laboratory network because multiple stakeholders with in-depth knowledge and experience in one or more tool sections were interviewed.The LABNET scorecard assessment was a useful addition to the ATLAS survey as it assessed the functionality of the laboratory network through quantitative responses that served as a reflection of the overall advancement toward targets set by IHR (2005) and GHSA.The lowest LABNET score was obtained in the ‘Legal and regulatory framework’ assessment; the Ghana National Health Laboratory Policy although approved has not yet been implemented, driving the lack of harmonised test menu, reagents and equipment. Thus, the laboratory policy needs to be implemented to ensure an adequate workforce and standardisation of laboratories. A weak legal and regulatory framework impacts all other LABNET core components and must be addressed first.Vertical programmes rely heavily on external funding, whereas health facilities solely rely on internally generated funds for laboratory supply procurement and service delivery. This presents the opportunity for a better collaboration with vertical programmes to access external funding or adapt the funding mechanisms of health facilities through targeted policy and administrative interventions.The supervision and quality regulation organised by the administrative units and vertical programmes align with ISO standards and could be leveraged for the accreditation of laboratories.The assessment found a collaboration between disease control programmes, clinical and PHLs in the forecasting, procurement and distribution of laboratory supplies, testing for priority target disease conditions, and supportive supervision for laboratory testing. The assessment also found NHIS reimbursement on diagnostic services and tests listed on the benefits package as an opportunity to increase local financing of laboratory services.18 have been addressed over the years through the development and implementation of the Policy for Infection Prevention and Control, and SOPs guiding inventory control and laboratory testing. It is commendable that the country has a national database into which data from clinical and PHLs are reported and easily accessed for decision-making. The LABNET assessment indicated that the laboratory information management system advanced the most toward achieving IHR and GHSA targets.Some gaps identified by the first ATLAS assessmentThe specimen transport arrangements between some target programmes (NTP and NACP) and Ghana Postal Courier Service serve as a good foundation to developing a specimen referral and transport system for all priority conditions.Laboratory funding was a major challenge indicated by most respondents. Target programmes rely heavily on external funding; the CLU reported that the internally generated funds and the often-delayed NHIS reimbursements were insufficient to maintain testing capacity . FundingAnother challenge is the delayed implementation of the Laboratory Policy approved in 2013. As a result, test menu harmonisation and reagents and equipment standardisation are a challenge, affecting specimen and patient referral to higher tiers. In addition, there is no organised system at the national level for sample transportation except for HIV and tuberculosis diagnoses.This assessment was carried out to characterise the laboratory network in Ghana using the ATLAS, a qualitative tool. In addition, some aspects of the LABNET scorecard were used to measure functionality quantitatively in support of findings on five out of the nine core capabilities that were related to some aspects of the ATLAS administered.Ideally, the entire qualitative and quantitative LABNET evaluation should be applied as part of a multisectoral workshop led by the MoH, with results based on consensus. Thus, future assessments should consider administering the entire LABNET tool and convene workshops to validate findings.Laboratories under the Ghana Health Service are tiered into three levels with the clinical laboratories vertically coordinated by the CLU whereas the Public Health Unit coordinates the PHLs. The laboratories collaborated with target programmes in the forecasting, procurement and distribution of laboratory supplies, supportive supervision and testing for priority conditions. No laboratory had yet received ISO accreditation at the time of assessment although some had been assessed using the Stepwise Laboratory Improvement Process Towards Accreditation checklist. In terms of functionality, the laboratory information management system advanced the most toward achieving IHR and GHSA targets whereas the political, legal, regulatory and financial framework lagged behind with the finance component at the lowest stage of functionality. Major gaps were identified in laboratory financing and the implementation of the National Health Laboratory Policy. These should represent the focus of future initiatives to strengthen the laboratory network in Ghana.Stakeholders recommended that the funding landscape for the country be reviewed. Considerations should be made to fund laboratory services from the country’s internally generated funds and expand the scope of NHIS. Also, the MoH should create a laboratory unit to collaborate with administrative units for laboratories in all their agencies to update and implement the National Health Laboratory Policy to guide improvement of the laboratory network towards achieving the GHSA targets. Moreover, plans for a national sample referral and transportation system that accommodate a wide range of clinical testing needs should be operationalised. The collaboration between administrative units and target programmes could be improved through joint supervision to optimise the resources available. Also, the quality regulation in laboratories align with ISO standards and could be leveraged for the accreditation of laboratories. A follow up LABNET assessment could be conducted to help document the laboratory network’s progress towards attaining the GHSA targets on all nine core capabilities.


Background: Evidence in adults suggests that exposure to artificial light-at-night (ALAN) leads to obesity. However, little is known about whether this effect exists in children and adolescents. We aimed to investigate whether ALAN exposure was associated with overweight and obesity in school-aged children and adolescents and whether this association varied with socioeconomic status. Methods: Data on the height and weight of 129,500 children and adolescents aged 10–18 years from 72 cities were extracted from the 2014 Chinese National Survey on Students’ Constitution and Health (CNSSCH). The ALAN area percentage and average ALAN intensity were calculated using the Visible/Infrared Imager/Radiometer Suite. The subjects were separated into three categories based on the cities’ gross domestic product per capita (GDPPC). A mixed-effect logistic regression model and generalized additive model (GAM) were utilized to evaluate the association between ALAN exposure and overweight and obesity in children and adolescents stratified by municipal GDPPC. Results: Both ALAN area  and ALAN intensity  were positively associated with overweight and obesity in children and adolescents, and the associations remained robust after adjusting for covariates. ORs for overweight and obesity and ALAN area decreased as GDPPC level increased . Similar results were observed for ALAN intensity. In the GAM models, thresholds existed in almost all these spline trends, indicating that ALAN might have a nonlinear association with overweight and obesity. Conclusions: ALAN contributed to the development of overweight and obesity in children and adolescents and this effect differed with GDPPC. Future longitudinal studies should confirm the causal relationship between ALAN and obesity. Moreover, reducing unnecessary exposure to artificial light at night may have beneficial implications for controlling childhood and adolescent obesity, particularly in low-income areas. Overweight and obesity in childhood and adolescence have become a major public health issue globally, as they can induce type 2 diabetes, metabolic abnormalities, cardiovascular disease, and even cancer in adulthood, resulting in severe medical burdens ,2. ChinaALAN is one of the environmental endocrine disruptors that have changed most obviously in recent decades. In 2016, ALAN covered 25% of the world’s land area and more than 80% of people were exposed to it , while CIn the present study, we investigated whether ALAN exposure is associated with overweight and obesity in children and adolescents and whether this association varies with GDPPC level using nationally representative data from the 2014 Chinese National Survey of Students’ Constitution and Health (CNSSCH). We hypothesized that exposure to ALAN would be positively associated with overweight and obesity in children and adolescents, and the association would differ when stratified by GDPPC.We investigated the nutritional status of school-aged children and adolescents using data from the 2014 Chinese National Survey of Students’ Constitution and Health (CNSSCH), the largest national cross-sectional survey of students, which was conducted in 30 provinces, autonomous regions, and municipalities in China . The CNSSCH sampling procedures were employed following  and are This study included 129,500 Han children and adolescents aged 10–18 years with complete city and basic characteristics information. The project was approved by the Medical Research Ethics Committee of the Peking University Health Science Center (IRB00001052-19095). Informed consent was obtained from both children and their parents. Participants’ information was anonymized and de-identified prior to analysis to protect their privacy.Launched on 28 October 2011, the Visible/Infrared Imager/Radiometer Suite (VIIRS) collected high-quality images at night at a spatial resolution of 750 m in the 500–900 nm day/night bands (DNBs)  are now 2) standards defined by the WHO [Height (in cm) and weight (in kg) were measured by trained technicians using a standardized procedure. Participants were asked to wear light clothing and stand on the measuring instrument without shoes to ensure the accuracy of the measurement. In order to control for measurement errors, the researchers repeated the measurements of height and weight for 3 percent of the randomly selected participants. Body mass index (BMI) was then calculated as the weight (kg) divided by the square of the height (m). Age- and sex-specific BMI Z-scores were also calculated. We analyzed overweight (>+1 SD for BMI Z-score) and obesity (>+2 SD for BMI Z-score) using the age- and sex-specific BMI . Prior to the questionnaire survey, the researchers introduced the content of the survey and provided enough time to ensure that children and adolescents had a good understanding of the survey. Researchers were available to provide appropriate assistance and guidance as needed. Epidata version 3.1  was used to input the answers for all questionnaires. All survey items were checked for completeness and accuracy by field surveyors and program directors, and an overall survey response of 99%, moderate internal consistency, and Cronbach’s α reliability coefficient of 0.68 were achieved . In addiWe generated several contextual covariates, including sociodemographic information and individual lifestyle factors. We also collected information on the municipal development indicators measured by the GDPPC and resident populations from the National Bureau of Statistics of China . The GDPp value of < 0.05 was considered statistically significant. Little’s Missing Completely At Random (MCAR) test was used to test whether samples were randomly missing. Quantitative variables were analyzed as medians (interquartile ranges) according to the normality of distribution, and qualitative variables were analyzed as numbers (percentages). The Kruskal–Wallis test and chi-squared test were used to compare difference between sub-groups. Scatter plots were used to validate correlations between ALAN indicators and overweight and obesity for each city in 2014. Mixed-effect logistic regression models were applied, and adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for associations between ALAN indicators and overweight and obesity stratified by various GDPPC levels. Generalized additive models (GAMs) were further used to calculate the nonlinear fitting curve of the coefficients of the ALAN indicators and overweight and obesity to quantify differences between different GDPPC levels. To confirm the robustness of the different GDPPC stratifications, we performed a mixed-effect logistic regression analysis based on the World Bank classification method published in 2013. The effects of sociodemographic characteristics , individual life factors , and municipal population information, as well as the clustering effects of schools, were adjusted for all model analyses. All statistical analyses were performed with ArcGIS version 10.6  and R version 4.1.0 . A two-tailed p > 0.05). We included 129,500 participants with complete records for basic characteristics and questionnaire information in the final analyses (p < 0.001).A total of 30,922 cases were excluded due to mismatched city information and 151 cases were excluded due to insufficient information on age and individual factors, but the loss was found to be random after employing Little’s MCAR test . Overweight and obesity were more strongly associated with ALAN area  . HoweverTo the best of our knowledge, this was the first study to evaluate the association between outdoor ALAN exposure and overweight and obesity in nationwide representative school-aged children and adolescents. We discovered that higher ALAN exposure was positively associated with increased risks of overweight and obesity, and the association differed between GDPPC levels after controlling for the influence of other confounding factors, such as GDPPC, eating habits, screen time, sleep duration, and physical activity. The results may provide evidence to help policymakers reduce the health burden of overweight and obesity in children and adolescents.Our findings add new evidence regarding the effects of outdoor ALAN exposure on overweight and obesity in children and adolescents. The results were consistent with the findings of previous adult studies. A global cross-sectional study found that outdoor ALAN has become a positive predictor of overweight and obesity in both sexes in over 80 countries around the world, and the effects of ALAN on overweight were comparable to those of junk food . Using aStronger associations between ALAN area and overweight and obesity were found in underdeveloped regions, while associations between ALAN intensity and overweight and obesity were stronger in highly developed regions. There was evidence that GDPPC was associated with an increase in childhood overweight and obesity ,39. AlthExisting evidence indicates that ALAN might influence body weight and metabolism in the following ways. Continuous exposure to ALAN affects the suprachiasmatic nucleus  , resultiGiven that ALAN is a single potential environmental risk factor, it does not affect the human body in the manner of direct toxicity or physical energy like other environmental risk factors, such as chemical poisons or radiation . TherefoWe utilized nationwide data to evaluate the association between ALAN and overweight and obesity in children and adolescents. However, this research had limitations. Firstly, satellite images of ALAN might not be able to reflect individual artificial light pollution exposure levels, which might be insufficient to influence the circadian rhythm. In addition, outdoor ALAN measured via satellite was not validated for individual ALAN exposure, since indoor ALAN from TVs, computers, and smartphones could also affect individuals ,38. A stIn summary, our analyses of a large population of Chinese school-aged children and adolescents suggested that ALAN might contribute to the development of overweight and obesity and that this effect differs with GDPPC level. We emphasize that ALAN should and likely will be a focus for children and adolescents with overweight and obesity. Reducing unnecessary ALAN exposure in early childhood, particularly in economically disadvantaged areas, and establishing a regular rest schedule may strengthen metabolic regulation in the future, with important implications for reducing childhood overweight and obesity.


High-throughput plant phenotyping can accelerate the development and characterization of new biostimulants. Contrarily, biostimulants can bring new challenges to plant phenotyping to develop complex approaches integrating -omics and data analysis. Commercial interest in biostimulants as a tool for sustainable green economics and agriculture concepts is on a steep rise, being followed by increasing demand to employ efficient scientific methods to develop new products and understand their mechanisms of action. Biostimulants represent a highly diverse group of agents derived from various natural sources. Regardless of their nutrition content and composition, they are classified by their ability to improve crop performance through enhanced nutrient use efficiency, abiotic stress tolerance, and quality of crops. Numerous reports have described modern, non-invasive sensor-based phenotyping methods in plant research. This review focuses on applying phenotyping approaches in biostimulant research and development, and maps the evolution of interaction of these two intensively growing domains. How phenotyping served to identify new biostimulants, the description of their biological activity, and the mechanism/mode of action are summarized. Special attention is dedicated to the indoor high-throughput methods using model plants suitable for biostimulant screening and developmental pipelines, and high-precision approaches used to determine biostimulant activity. The need for a complex method of testing biostimulants as multicomponent products through integrating other -omic approaches followed by advanced statistical/mathematical tools is emphasized. This definition is consistent with the definition of EU Fertilising Products Regulation 2019/1009 describing ‘a plant biostimulant shall be an EU fertilising product the function of which is to stimulate plant nutrition processes independently of the product’s nutrient content with the sole aim of improving one or more of the following characteristics of the plant or the plant rhizosphere: nutrient use efficiency, tolerance to abiotic stress, quality traits, or availability of confined nutrients in the soil or rhizosphere’ .‘Plant biostimulants’ is a hypernym used to describe very different substances such as seaweed extracts, humic and fulvic acids, animal- and vegetal-based protein hydrolysates, and microorganisms such as mycorrhizal fungi and rhizospheric bacteria. The definition of plant biostimulants has beeen the subject of extensive discussions and evolution to promote the acceptance of biostimulants by future regulations  of ~12% during the next 5 years to reach more than US$ 5 billion by 2027. This continually increasing commercial interest in the development and use of biostimulant products, driven by economic and socio-political factors, has been followed by entering the regulatory framework to establish clear standards regarding the claims made by biostimulant producers. For example, in Europe, a CE-certified biostimulant can be placed in the EU Single Market if it fulfills the safety requirements of Regulation (EU) 2019/1009. Besides, the producer must demonstrate plant biostimulant effect to justify the claims following the EU-harmonized standards defined by the Committee for European Standardization (CEN). The biostimulant trials are becoming more demanding and necessitate rigorous and professional practices to fulfill the claims.Along with biostimulant development, companies have started interacting with the scientific domain and investing significant parts of their revenues into their research and development activities. Scientific approaches that help identify efficient sources for developing new products, clearly and rigorously describe their effects, and, importantly, help understand their mechanism of action are wanted. Testing biostimulant effects in field conditions is highly relevant and unreplaceable; however, some of the claims, such as abiotic stress tolerance, can hardly be justified in the same field trial. In this regard, modern plant phenotyping using non-invasive digital technologies can speed up the development and characterization of the new biostimulants using high-throughput and high-precision approaches in controlled and semi-controlled conditions. They allow multidimensional testing combining different types of application in a broad range of concentrations on plants subjected to several growth conditions, individually or in combination. This review analyzes the evolution of using such approaches in an intensively growing biostimulant research and development domain.Literally, ‘plant phenotyping’ refers to a quantitative description of the plant’s anatomical, ontogenetical, physiological, and biochemical properties resulting from genotypic differences and the environmental conditions to which a plant has been exposed. The use of this term in the scientific literature has been evolving. To overview this evolution, we searched under the Basic Search tab on the Web of Science ‘plant AND phenotyping’ in all fields and refined the results by categories; Plant Science, Agronomy, Horticulture, Environmental Science, and Agriculture Multidisciplinary. We obtained 3826 results, including 3255 original articles, 396 review articles, and 171 proceeding papers. The term plant phenotyping was first mentioned in Arabidopsis thaliana’. Next, the evolution of these terms over time was analyzed using overlay visualization  as described by lization . Since tlization  predominlization  focused lization , surprislization , D. SeveWe searched under the Basic Search tab of the Web of Science ‘plant AND biostimulant OR biostimulator’ using the same criteria. We obtained 977 results, including 750 articles, 98 review articles, and 111 proceedings papers. The first use of the term ‘plant biostimulant’ appeared in The 977 publications related to plant biostimulants were also analyzed using VOSviewer. Due to the lower number of publications, the minimum number of occurrences per term was reduced to 30. Again a map formed by three main clusters was obtained: a red cluster contained almost half of the items (49), a blue cluster with 36 items, and a green cluster with 18 items . The redActa Horticulturae by Plant phenotyping and biostimulants generated interest in the scientific communities simultaneously. However, no interconnections between both terms appear in the VOS analysis. To evaluate the degree of connection between both scientific communities, we performed a new search on the Web of Science using ‘plant AND phenotyping AND biostimulant OR biostimulator’ as keywords. We found only 16 results, comprising 12 original works and four review articles , 2. MoreThe 16 identified publications using ‘plant AND phenotyping AND biostimulant OR biostimulator’ as keywords were analyzed using VOSviewer to understand the actual state of the art of plant phenotyping and biostimulant connection. Due to the low number of works, this time, the minimum number of occurrences was reduced to four, ending up with 17 items divided into two clusters; red (nine) and green (eight) . The firAnother critical point nowadays is the evolution of the biostimulant-based products, so they are more focused on complex substances  than on simple natural molecules (i.e. plant hormones or specific amino acids). Thus, the complexity of the new biostimulants due to their natural origin (i.e. seaweeds), the raw material , and/or the preparation procedure needs an in-depth study to understand not only their mode of action but also the stability of the batches and viability of the final products. Plant phenotyping has been identified as a beneficial technology for simultaneously testing different batches, extraction processes, and final products, thanks to the high-throughput screening (HTS) approaches  cameras installed in a microphenotron (cabinet)  that can be integrated to calculate a unique number called the Plant Biostimulant Characterization (PBC) index for a more straightforward classification of biostimulants as plant growth promotors and/or stress alleviators, or growth inhibitors  is the favorite species for studying biostimulants using plant phenotyping methods. In tomato plants, biostimulants from different sources [complex biostimulants based on a crude bioextract obtained from microalgae or cyanobacteria  showed contrasting results: CL-SW improved plant transpiration, biomass, and final yield under control conditions, whereas ICL-NewFo1 delayed water loss under drought stress (Triticum turgidum L. subsp. durum (Desf) Husn. with thyme essential oil or the endophyte Paraburkholderia phytofirmans (PsJN) activated different strategies in the plants to deal with the water–nutrient stress  or a prepared formulation (ICL-NewFo1) in peppers  de Bary, and Sclerotium rolfsii Sacc. growth in vitro and to study the effect of these microbes on plants in vivo using baby lettuce as a host  or their combination for the phenotyping experiment can offer the appropriate tool for characterizing the biostimulant’s mechanism(s) of action. Even if biostimulants are mostly complex multicomponent products, we believe that advanced plant phenotyping combined with various -omics approaches followed by data analysis using advanced statistical tools can be used to unravel the potential biostimulant mode of action. Untargeted metabolomics was shown to be a valuable technique to be combined with phenomics for characterizing the potential mode of action of different biostimulants applied to Arabidopsis , revealing activation of different species-specific responses  Plant phenotyping: plant breeding (QTL) → new non-invasive systems to phenotype plants data analysis and deep learning. (ii) Biostimulants: testing products in simple assays → new parameters studied under controlled conditions or stress → mechanism of action in plants under salt and drought conditions. (iii) Plant phenotyping and biostimulants: new approaches for testing crop development induced by biostimulants → type of treatment and their mode of action.Practically, plant phenotyping technologies seem to become essential for the biostimulant industry. They can significantly contribute to: (i) basic research of biostimulants, and characterization of their mechanism/mode of action; (ii) screening campaigns for the identification of new biostimulants from new raw materials and beneficial microorganisms; (iii) developmental pipelines for analyses of batches and testing of a new method of preparation; (iv) production pipelines for shelf-life analysis and quality control; and (v) justification of the claims requested by certification authorities,On the contrary, testing biostimulants as complex multicomponent products can stimulate the need to develop new complex phenotyping approaches and methodologies integrating different -omics and robust data analysis tools such as multivariate statistical approaches and artificial intelligence. Multidimensional biostimulant testing combining different application types  in broad concentration ranges in multiple environmental conditions requires a high-throughput capacity for testing hardware and analysis software. Higher testing capacities of phenotyping facilities and/or development of affordable devices will be needed for high-throughput phenotype-based screening and data analysis ready for integration into the biostimulant R&D industries’ pipelines. Moreover, we believe that plant phenotyping should reconnect to plant breeding. By doing so, it will open a new perspective of biostimulant testing, offering the possibility of developing targeted biostimulants with a specific activity in crops grown in conditions for which they were primarily selected by breeding programs. An ideal interconnection among the plant phenotyping scientific community, biostimulant industry, and plant breeders is presented in