Recent advances in remote sensing hyperspectral imaging and artificial intelligence (AI) bring exciting opportunities to various fields of science and industry that can directly benefit from in-orbit data processing. Taking AI into space may accelerate the response to various events, as massively large raw hyperspectral images (HSIs) can be turned into useful information onboard a satellite; hence, the images’ transfer to the ground becomes much faster and offers enormous scalability of AI solutions to areas across the globe. However, there are numerous challenges related to hardware and energy constraints, resource frugality of (deep) machine learning models, availability of ground truth data, and building trust in AI-based solutions. Unbiased, objective, and interpretable selection of an AI application is of paramount importance for emerging missions, as it influences all aspects of satellite design and operation. In this article, we tackle this issue and introduce a quantifiable procedure for objectively assessing potential AI applications considered for onboard deployment. To prove the flexibility of the suggested technique, we utilize the approach to evaluate AI applications for two fundamentally different missions: the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) [European Union/European Space Agency (ESA)] and the 6U nanosatellite Intuition-1 (KP Labs). We believe that our standardized process may become an important tool for maximizing the outcome of Earth observation (EO) missions through selecting the most relevant onboard AI applications in terms of scientific and industrial outcomes.

Context: The presence of metabolic dysfunction–associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a high risk of cardiovascular disease, but is often underdiagnosed.Objective: To develop machine learning (ML) models for risk assessment of MASLD occurrence in patients with DM.Methods: Feature selection determined the discriminative parameters, utilized to classify DM patients as those with and without MASLD. The performance of the multiple logistic regression model was quantified by sensitivity, specificity, and percentage of correctly classified patients, and receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) assessed the model's net benefit for alternative treatments.Results: We studied 2000 patients with DM (mean age 58.85 ± 17.37 years; 48% women). Eight parameters: age, body mass index, type of DM, alanine aminotransferase, aspartate aminotransferase, platelet count, hyperuricaemia, and treatment with metformin were identified as discriminative. The experiments for 1735 patients show that 744/991 (75.08%) and 586/744 (78.76%) patients with/without MASLD were correctly identified (sensitivity/specificity: 0.75/0.79). The area under ROC (AUC) was 0.84 (95% CI, 0.82-0.86), while DCA showed a higher clinical utility of the model, ranging from 30% to 84% threshold probability. Results for 265 test patients confirm the model's generalizability (sensitivity/specificity: 0.80/0.74; AUC: 0.81 [95% CI, 0.76-0.87]), whereas unsupervised clustering identified high-risk patients.Conclusion: A ML approach demonstrated high performance in identifying MASLD in patients with DM. This approach may facilitate better risk stratification and cardiovascular risk prevention strategies for high-risk patients with DM at risk of MASLD.

For decades, metformin has been the drug of first choice in the management of type 2 diabetes. However, approximately 2-13% of patients do not tolerate metformin due to gastrointestinal (GI) side effects. Since metformin influences the gut microbiota, we hypothesized that a multi-strain probiotics supplementation would mitigate the gastrointestinal symptoms associated with metformin usage. This randomized, double-blind, placebo-controlled, single-center, cross-over trial (ProGasMet study) assessed the efficacy of a multi-strain probiotic in 37 patients with metformin intolerance. Patients were randomly allocated (1:1) to receive probiotic (PRO-PLA) or placebo (PLA-PRO) at baseline and, after 12 weeks (period 1), they crossed-over to the other treatment arm (period 2). The primary outcome was the reduction of GI adverse events of metformin. 37 out of 82 eligible patients were enrolled in the final analysis of whom 35 completed the 32 weeks study period and 2 patients resigned at visit 5. Regardless of the treatment arm allocation, while on probiotic supplementation, there was a significant reduction of incidence (for the probiotic period in PRO-PLA/PLA-PRO: P = 0.017/P = 0.054), quantity and severity of nausea (P = 0.016/P = 0.024), frequency (P = 0.009/P = 0.015) and severity (P = 0.019/P = 0.005) of abdominal bloating/pain as well as significant improvement in self-assessed tolerability of metformin (P < 0.01/P = 0.005). Moreover, there was significant reduction of incidence of diarrhea while on probiotic supplementation in PRO-PLA treatment arm (P = 0.036). A multi-strain probiotic diminishes the incidence of gastrointestinal adverse effects in patients with type 2 diabetes and metformin intolerance.

With the aging population of the developed countries, the diseases of affluence become common. One of the consequences of their complications are chronic wounds that affect more and more people. Because the healing of chronic wounds is a lengthy process, its monitoring should be as simple as possible, yet providing accurate and reliable documentation. In this paper, we present an image acquisition system and wound surface reconstruction method using several imaging modalities: color photography, thermal imaging, and depth perception. The proposed method is dedicated mainly to wounds located on the limbs where body curvature is large, and its influence on 2D results cannot be neglected. Our approach minimizes the extra effort taken by the medical staff to prepare the wound outline as it is still performed using 2D color photo and then mapped into the 3D space. The method was validated on 29 data sets containing two 3D point clouds from two depth imaging devices (depth camera and stereo camera) as well as 2D color photos and thermal maps. The approach was compared with expert delineations as well as other contemporary methods for wound surface reconstruction presented in the literature. Performed experiments and the obtained results show that the proposed method is statistically concordant with expert delineations performed in 3D.

Recent advancements in hyperspectral remote sensing bring exciting opportunities for various domains. Precision agriculture is one of the most widely-researched examples here, as it can benefit from the non-invasiveness and enormous scalability of the Earth observation solutions. In this paper, we focus on estimating the chlorophyll level in leaves using hyperspectral images—capturing this information may help farmers optimize their agricultural practices and is pivotal in planning the plants’ treatment procedures. Although there are machine learning algorithms for this task, they are often validated over private datasets; therefore, their performance and generalization capabilities are virtually impossible to compare. We tackle this issue and introduce an open dataset including the hyperspectral and in situ ground-truth data, together with a validation procedure which is suggested to follow while investigating the emerging approaches for chlorophyll analysis with the use of our dataset. The experiments not only provided the solid baseline results obtained using 15 machine learning models over the introduced training-test dataset splits but also showed that it is possible to substantially improve the capabilities of the basic data-driven models. We believe that our work can become an important step toward standardizing the way the community validates algorithms for estimating chlorophyll-related parameters, and may be pivotal in consolidating the state of the art in the field by providing a clear and fair way of comparing new techniques over real data.

Enhancing agricultural methods through the utilization of Earth observation and artificial intelligence (AI) has emerged as a significant concern. The ability to quantify soil parameters on a large scale can play a pivotal role in optimizing the fertilization process. While techniques for noninvasive estimation of soil parameters from hyperspectral images (HSIs) exist, their validation typically occurs across different datasets and employs varying validation protocols. This diversity renders them inherently challenging (or even impossible) to compare objectively.