The effect of transcutaneous auricular vagus nerve stimulation on cardiovascular function in subarachnoid hemorrhage patients: a safety study.

Objective: Subarachnoid hemorrhage (SAH) is characterized by intense central inflammation, leading to substantial post-hemorrhagic complications such as vasospasm and delayed cerebral ischemia.2,6,7 Given the anti-inflammatory effect of transcutaneous auricular vagus nerve stimulation (taVNS) and its ability to promote brain plasticity, taVNS has emerged as a promising therapeutic option for SAH patients.3,8,9 However, the effects of taVNS on cardiovascular dynamics in critically ill patients like those with SAH have not yet been investigated. Given the association between cardiac complications and elevated risk of poor clinical outcomes after SAH, it is essential to characterize the cardiovascular effects of taVNS to ensure this approach is safe in this fragile population4. Therefore, we assessed the impact of both acute taVNS and repetitive taVNS on cardiovascular function in this study. Methods: In this randomized clinical trial, 24 SAH patients were assigned to either a taVNS treatment or a Sham treatment group. During their stay in the intensive care unit, we monitored patient electrocardiogram (ECG) readings and vital signs. We compared long-term changes in heart rate, heart rate variability, QT interval, and blood pressure between the two groups. Additionally, we assessed the effects of acute taVNS by comparing cardiovascular metrics before, during, and after the intervention. We also explored rapidly responsive cardiovascular biomarkers in patients exhibiting clinical improvement. Results: We found that repetitive taVNS did not significantly alter heart rate, corrected QT interval, blood pressure, or intracranial pressure. However, taVNS increased overall heart rate variability and parasympathetic activity from 5-10 days after initial treatment, as compared to the sham treatment. Acutely, taVNS increased heart rate, blood pressure, and peripheral perfusion index without affecting the corrected QT interval, intracranial pressure, or heart rate variability. The acute post-treatment elevation in heart rate was more pronounced in patients who experienced a decrease of more than 1 point in their Modified Rankin Score at the time of discharge. Conclusions: Our study found that taVNS treatment did not induce adverse cardiovascular effects, such as bradycardia or QT prolongation, supporting its development as a safe immunomodulatory treatment approach for SAH patients. The observed acute increase in heart rate after taVNS treatment may serve as a biomarker for SAH patients who could derive greater benefit from this treatment.

Using Deep Learning to Automate Eosinophil Counting in Pediatric Ulcerative Colitis Histopathological Images.

Background: Accurate identification of inflammatory cells from mucosal histopathology images is important in diagnosing ulcerative colitis. The identification of eosinophils in the colonic mucosa has been associated with disease course. Cell counting is not only time-consuming but can also be subjective to human biases. In this study we developed an automatic eosinophilic cell counting tool from mucosal histopathology images, using deep learning. Method: Four pediatric IBD pathologists from two North American pediatric hospitals annotated 530 crops from 143 standard-of-care hematoxylin and eosin (H & E) rectal mucosal biopsies. A 305/75 split was used for training/validation to develop and optimize a U-Net based deep learning model, and 150 crops were used as a test set. The U-Net model was then compared to SAU-Net, a state-of-the-art U-Net variant. We undertook post-processing steps, namely, (1) the pixel-level probability threshold, (2) the minimum number of clustered pixels to designate a cell, and (3) the connectivity. Experiments were run to optimize model parameters using AUROC and cross-entropy loss as the performance metrics. Results: The F1-score was 0.86 (95%CI:0.79-0.91) (Precision: 0.77 (95%CI:0.70-0.83), Recall: 0.96 (95%CI:0.93-0.99)) to identify eosinophils as compared to an F1-score of 0.2 (95%CI:0.13-0.26) for SAU-Net (Precision: 0.38 (95%CI:0.31-0.46), Recall: 0.13 (95%CI:0.08-0.19)). The inter-rater reliability was 0.96 (95%CI:0.93-0.97). The correlation between two pathologists and the algorithm was 0.89 (95%CI:0.82-0.94) and 0.88 (95%CI:0.80-0.94) respectively. Conclusion: Our results indicate that deep learning-based automated eosinophilic cell counting can obtain a robust level of accuracy with a high degree of concordance with manual expert annotations.

Optical coherence tomography angiography analysis methods: a systematic review and meta-analysis.

Optical coherence tomography angiography (OCTA) is widely used for non-invasive retinal vascular imaging, but the OCTA methods used to assess retinal perfusion vary. We evaluated the different methods used to assess retinal perfusion between OCTA studies. MEDLINE and Embase were searched from 2014 to August 2021. We included prospective studies including ≥ 50 participants using OCTA to assess retinal perfusion in either global retinal or systemic disorders. Risk of bias was assessed using the National Institute of Health quality assessment tool for observational cohort and cross-sectional studies. Heterogeneity of data was assessed by Q statistics, Chi-square test, and I2 index. Of the 5974 studies identified, 191 studies were included in this evaluation. The selected studies employed seven OCTA devices, six macula volume dimensions, four macula subregions, nine perfusion analyses, and five vessel layer definitions, totalling 197 distinct methods of assessing macula perfusion and over 7000 possible combinations. Meta-analysis was performed on 88 studies reporting vessel density and foveal avascular zone area, showing lower retinal perfusion in patients with diabetes mellitus than in healthy controls, but with high heterogeneity. Heterogeneity was lowest and reported vascular effects strongest in superficial capillary plexus assessments. Systematic review of OCTA studies revealed massive heterogeneity in the methods employed to assess retinal perfusion, supporting calls for standardisation of methodology.

Seasonal meropenem resistance in Acinetobacter baumannii and influence of temperature-driven adaptation.

BACKGROUND: Recognition of seasonal trends in bacterial infection and drug resistance rates may enhance diagnosis, direct therapeutic strategies, and inform preventive measures. Limited data exist on the seasonal variability of Acinetobacter baumannii. We investigated the seasonality of A. baumannii, the correlation between temperature and meropenem resistance, and the impact of temperature on this bacterium. RESULTS: Meropenem resistance rates increased with lower temperatures, peaking in winter/colder months. Nonresistant strain detection exhibited temperature-dependent seasonality, rising in summer/warmer months and declining in winter/colder months. In contrast, resistant strains showed no seasonality. Variations in meropenem-resistant and nonresistant bacterial resilience to temperature changes were observed. Nonresistant strains displayed growth advantages at temperatures ≥ 25 °C, whereas meropenem-resistant A. baumannii with ß-lactamase OXA-23 exhibited greater resistance to low-temperature (4 °C) stress. Furthermore, at 4 °C, A. baumannii upregulated carbapenem resistance-related genes (adeJ, oxa-51, and oxa-23) and increased meropenem stress tolerance. CONCLUSIONS: Meropenem resistance rates in A. baumannii display seasonality and are negatively correlated with local temperature, with rates peaking in winter, possibly linked to the differential adaptation of resistant and nonresistant isolates to temperature fluctuations. Furthermore, due to significant resistance rate variations between quarters, compiling monthly or quarterly reports might enhance comprehension of antibiotic resistance trends. Consequently, this could assist in formulating strategies to control and prevent resistance within healthcare facilities.

Experimental study on the effects of water content on the compression characteristics and particle breakage of calcareous sand.

The particle breakage effect and compression characteristics of calcareous sand are related to the water content in the sand material. However, the effects of water content on the particle breakage and compression characteristics of calcareous sand have rarely been investigated. In this work, 50 sets of confined compression tests were conducted on calcareous sand specimens, and the compression characteristics and particle breakage effects of two single-particle-size groups (particle size ranges of 1-0.5 mm and 0.5-0.25 mm) of calcareous sand were investigated under five different water contents. The test results showed that with the increase in the water content, the final compression deformation of calcareous sand was positively correlated with the water content. The final compression deformation decreased when the water content reached a certain value. The water content corresponding to the peak final compression deformation was related to the gradation of the calcareous sand; the specific values were 10% and 15% for particle size ranges of 1-0.5 mm and 0.5-0.25 mm, respectively. With the increase in the water content, the slope of the loading curve of calcareous sand appeared to increase and then decrease, reaching maximum when the water content was 10%. Moreover, the slope of the loading curve was close to twice that of the loading curve of dry sand, whereas the slope of the unloading curve changed little. Under the same water content, the initial gradation had no effect on the compression and unloading characteristics of the specimens beyond a vertical pressure of 1 MPa. The effects of the variation in the water content on the particle breakage of calcareous sand were mainly reflected in the softening effect of water on the specimen particles, which reduced the Mohr strength of the particles.

AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices.

BACKGROUND: Artificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices. There is therefore value in understanding the level of evidence that underpins AIaMD currently on the market, as a step toward identifying what the best practices might be in this area. In this systematic scoping review, we will focus on AIaMD that contributes to clinical decision-making (relating to screening, diagnosis, prognosis, and treatment) in the context of ophthalmic imaging. OBJECTIVE: This study aims to identify regulator-approved AIaMD for ophthalmic imaging in Europe, Australia, and the United States; report the characteristics of these devices and their regulatory approvals; and report the available evidence underpinning these AIaMD. METHODS: The Food and Drug Administration (United States), the Australian Register of Therapeutic Goods (Australia), the Medicines and Healthcare products Regulatory Agency (United Kingdom), and the European Database on Medical Devices (European Union) regulatory databases will be searched for ophthalmic imaging AIaMD through a snowballing approach. PubMed and clinical trial registries will be systematically searched, and manufacturers will be directly contacted for studies investigating the effectiveness of eligible AIaMD. Preliminary regulatory database searches, evidence searches, screening, data extraction, and methodological quality assessment will be undertaken by 2 independent review authors and arbitrated by a third at each stage of the process. RESULTS: Preliminary searches were conducted in February 2023. Data extraction, data synthesis, and assessment of methodological quality commenced in October 2023. The review is on track to be completed and submitted for peer review by April 2024. CONCLUSIONS: This systematic review will provide greater clarity on ophthalmic imaging AIaMD that have achieved regulatory approval as well as the evidence that underpins them. This should help adopters understand the range of tools available and whether they can be safely incorporated into their clinical workflow, and it should also support developers in navigating regulatory approval more efficiently. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52602.

Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study.

BACKGROUND: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. OBJECTIVE: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. METHODS: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "definitely include," and suggest edits. The document will be iterated between rounds based on participants' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. RESULTS: Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. CONCLUSIONS: The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50568.

Suppressed Phase Segregation with Small A-Site and Large X-Site Incorporation for Photostable Wide-Bandgap Perovskite Solar Cells.

Wide-bandgap (WBG) perovskite solar cells (PSCs) have been widely used as the top cell of tandem solar cells. However, photoinduced phase segregation and high open-circuit voltage loss pose significant obstacles to the development of WBG PSCs. Here, a two-step small-size A-site and large-size X-site incorporation strategy is reported to modulate the lattice distortion and improve the film quality of WBG formamidinium-methylammonium (FAMA) perovskite films for photostable PSCs based on two-step deposition method. First, CsI with content of 0-20% is introduced to tune the lattice distortion and film quality of FAMA perovskite with a bandgap of 1.70 eV. Then, 4% RbI is incorporated to further modulate the perovskite growth and lattice distortion, leading to the suppression of photoinduced phase segregation in the resultant RbCsFAMA quadruple cation perovskites. As a result, the 20%CsI/4%RbI-doped device obtains a promising efficiency of 20.6%, and the corresponding perovskite film shows good photothermal stability. Even without encapsulation, the device can maintain 92% of its initial efficiency after 1000 h of continuous operation under 1 sun equivalent white light-emitting diode illumination.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

[RESUMEN]. La declaración SPIRIT 2013 tiene como objetivo mejorar la exhaustividad de los informes de los protocolos de los ensayos clínicos proporcionando recomendaciones basadas en la evidencia para el conjunto mínimo de elementos que deben abordarse. Esta guía ha sido fundamental para promover la evaluación transparente de nuevas intervenciones. Más recientemente, se ha reconocido cada vez más que las intervenciones con inteligencia artificial (IA) deben someterse a una evaluación rigurosa y prospectiva para demostrar su impacto en los resultados médicos. La extensión SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence, por sus siglas en inglés) es una nueva directriz para el reporte de los protocolos de ensayos clínicos que evalúan intervenciones con un componente de IA. Esta directriz se desarrolló en paralelo con su declaración complemen- taria para los informes de ensayos clínicos: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Ambas directrices se desarrollaron a través de un proceso de consenso por etapas que incluía la revisión de la literatura y la consulta a expertos para generar 26 ítems candidatos, que fueron consultados por un grupo internacional de múltiples partes interesadas en una encuesta Delphi de dos etapas (103 partes interesadas), acordados en una reunión de consenso (31 partes interesadas) y refinados a través de una lista de verificación piloto (34 participantes). La ampliación de SPIRIT-AI incluye 15 nuevos elementos que se consideraron suficientemente importantes para los protocolos de los ensayos clínicos con intervenciones de IA. Estos nuevos ítems deben ser reportados rutinariamente además de los ítems centrales de SPIRIT 2013. SPIRIT-AI recomienda que los investigadores proporcionen descripciones claras de la intervención de IA, incluyendo las instrucciones y las habilidades necesarias para su uso, el entorno en el que se integrará la intervención de IA, las consideraciones para el manejo de los datos de entrada y salida, la interacción entre el ser humano y la IA y el análisis de los casos de error. SPIRIT-AI ayudará a promover la transparencia y la exhaustividad de los protocolos de los ensayos clínicos de las intervenciones de IA. Su uso ayudará a los editores y revisores, así como a los lectores en general, a comprender, interpretar y valorar críticamente el diseño y el riesgo de sesgo de un futuro ensayo clínico.

[ABSTRACT]. The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general reader- ship, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

[RESUMO]. A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens can- didatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

Leading in the development, standardised evaluation, and adoption of artificial intelligence in clinical practice: regional anaesthesia as an example.

A recent study by Suissa and colleagues explored the clinical relevance of a medical image segmentation metric (Dice metric) commonly used in the field of artificial intelligence (AI). They showed that pixel-wise agreement for physician identification of structures on ultrasound images is variable, and a relatively low Dice metric (0.34) correlated to a substantial agreement on subjective clinical assessment. We highlight the need to bring structure and clinical perspective to the evaluation of medical AI, which clinicians are best placed to direct.

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

Biodiversity responses to agricultural practices in cropland and natural habitats.

Largely driven by agricultural pressures, biodiversity has experienced great changes globally. Exploring biodiversity responses to agricultural practices associated with agricultural intensification can benefit biodiversity conservation in agricultural landscapes. However, the effects of agricultural practices may also extend to natural habitats. Moreover, agricultural impacts may also vary with geographical region. We analyze biodiversity responses to landscape cropland coverage, cropping frequency, fertiliser and yield, among different land-use types and across geographical regions. We find that species richness and total abundance generally respond negatively to increased landscape cropland coverage. Biodiversity reductions in human land-use types (pasture, plantation forest and cropland) were stronger in tropical than non-tropical regions, which was also true for biodiversity reductions with increasing yield in both human and natural land-use types. Our results underline substantial biodiversity responses to agricultural practices not only in cropland but also in natural habitats, highlighting the fact that biodiversity conservation demands a greater focus on optimizing agricultural management at the landscape scale.

Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review.

BACKGROUND: There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies. METHODS: We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022. We restricted our search to include studies that primarily used conventional imaging data, were undertaken in human participants, and published in English. Two reviewers independently reviewed the abstracts. The methodological quality of the studies was determined, and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). RESULTS: Forty studies were included, thirty-nine developed diagnostic models. Seven studies utilized ML approaches, six were retrospective and none used multicenter data for model development. Thirty-three studies utilized DL approaches, ten were prospective, and twelve multicenter studies. Overall, all studies demonstrated high risk of bias. ML studies were evaluated in 4 domains all rated as high risk of bias: participants (6/7), predictors (1/7), outcome (3/7), and analysis (7/7), and DL studies evaluated in 3 domains: participants (24/33), outcome (10/33), and analysis (18/33). The majority of image-based studies used colonoscopy images. CONCLUSION: The risk of bias was high in AI IBD image-based prediction models, owing to insufficient sample size, unreported missingness and lack of an external validation cohort. Models with a high risk of bias are unlikely to be generalizable and suitable for clinical implementation.

[Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI]. / Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI.

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

[Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionDiretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI]. / Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI.

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials ­ Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials ­ Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.

Effect of anisotropic brain conductivity on patient-specific volume of tissue activation in deep brain stimulation for Parkinson disease.

OBJECTIVE: Deep brain stimulation (DBS) modeling can improve surgical targeting by quantifying the spatial extent of stimulation relative to subcortical structures of interest. A certain degree of model complexity is required to obtain accurate predictions, particularly complexity regarding electrical properties of the tissue around DBS electrodes. In this study, the effect of anisotropy on the volume of tissue activation (VTA) was evaluated in an individualized manner. METHODS: Tissue activation models incorporating patient-specific tissue conductivity were built for 40 Parkinson disease patients who had received bilateral subthalamic nucleus (STN) DBS. To assess the impact of local changes in tissue anisotropy, one VTA was computed at each electrode contact using identical stimulation parameters. For comparison, VTAs were also computed assuming isotropic tissue conductivity. Stimulation location was considered by classifying the anisotropic VTAs relative to the STN. VTAs were characterized based on volume, spread in three directions, sphericity, and Dice coefficient. RESULTS: Incorporating anisotropy generated significantly larger and less spherical VTAs overall. However, its effect on VTA size and shape was variable and more nuanced at the individual patient and implantation levels. Dorsal VTAs had significantly higher sphericity than ventral VTAs, suggesting more isotropic behavior. Contrastingly, lateral and posterior VTAs had significantly larger and smaller lateral-medial spreads, respectively. Volume and spread correlated negatively with sphericity. CONCLUSION: The influence of anisotropy on VTA predictions is important to consider, and varies across patients and stimulation location. SIGNIFICANCE: This study highlights the importance of considering individualized factors in DBS modeling to accurately characterize the VTA.

Changes in isoleucine, sarcosine, and dimethylglycine during OGTT as risk factors for diabetes.

CONTEXT: Current metabolomics studies in diabetes have focused on the fasting state, while only few addressed the satiated state. OBJECTIVE: We combined oral glucose tolerance test (OGTT) and metabolomics to examine metabolite level changes in populations with different glucose tolerance statuses and evaluate the potential risk of these changes for diabetes. METHODS: We grouped participants into those with normal glucose tolerance (NGT), impaired glucose regulation (IGR), and newly diagnosed type 2 diabetes (NDM). During the OGTT, serum was collected at 0, 30, 60, 120, and 180 min. We evaluated the changes in metabolite levels during OGTT and compared metabolic profiles among the three groups. The relationship between metabolite levels during the OGTT and risk of diabetes and prediabetes was analyzed using generalized estimating equation (GEE). The regression results were adjusted for sex, body mass index, fasting insulin levels, heart rate, smoking status, and blood pressure. RESULTS: Glucose intake altered metabolic profile and induced an increase in glycolytic intermediates and decrease in amino acids, glycerol, ketone bodies, and triglycerides. Isoleucine levels differed between NGT and NDM groups and between NGT and IGR groups. Changes in sarcosine levels during OGTT in diabetes groups were opposite to those in glycine levels. GEE analysis revealed that during OGTT, isoleucine, sarcosine, and acetic acid levels were associated with NDM risks, while isoleucine and acetate levels with IGR risks. CONCLUSIONS: Metabolic profiles differ after glucose induction in individuals with different glucose tolerance statuses. Changes in metabolite levels during OGTT are potential risk factors for diabetes development.