Algor-ethics: charting the ethical path for AI in critical care.

The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of 'Human-in-the-Loop' and 'Algorithmic Stewardship' principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.

Learning Ordinal-Hierarchical Constraints for Deep Learning Classifiers.

Real-world classification problems may disclose different hierarchical levels where the categories are displayed in an ordinal structure. However, no specific deep learning (DL) models simultaneously learn hierarchical and ordinal constraints while improving generalization performance. To fill this gap, we propose the introduction of two novel ordinal-hierarchical DL methodologies, namely, the hierarchical cumulative link model (HCLM) and hierarchical-ordinal binary decomposition (HOBD), which are able to model the ordinal structure within different hierarchical levels of the labels. In particular, we decompose the hierarchical-ordinal problem into local and global graph paths that may encode an ordinal constraint for each hierarchical level. Thus, we frame this problem as simultaneously minimizing global and local losses. Furthermore, the ordinal constraints are set by two approaches ordinal binary decomposition (OBD) and cumulative link model (CLM) within each global and local function. The effectiveness of the proposed approach is measured on four real-use case datasets concerning industrial, biomedical, computer vision, and financial domains. The extracted results demonstrate a statistically significant improvement to state-of-the-art nominal, ordinal, and hierarchical approaches.

A preliminary study on self-care telemonitoring of dysarthria in spinal muscular atrophy.

Spinal muscular atrophy (SMA) is a rare neuromuscular disease which may cause impairments in oro-facial musculature. Most of the individuals with SMA present bulbar signs such as flaccid dysarthria which mines their abilities to speak and, as consequence, their psychic balance. To support clinicians, recent work has demonstrated the feasibility of video-based techniques for assessing the oro-facial functions in patients with neurological disorders such as amyotrophic lateral sclerosis. However, no work has so far focused on automatic and quantitative monitoring of dysarthria in SMA. To overcome limitations this work's aim is to propose a cloud-based store-and-forward telemonitoring system for automatic and quantitative evaluation of oro-facial muscles in individuals with SMA. The system integrates a convolutional neural network (CNN) aimed at identifying the position of facial landmarks from video recordings acquired via a web application by an SMA patient.Clinical relevance- The proposed work is in the preliminary stage, but it represents the first step towards a better understanding of the bulbar-functions' evolution in patients with SMA.

A deep learning-based telemonitoring application to automatically assess oral diadochokinesis in patients with bulbar amyotrophic lateral sclerosis.

BACKGROUND AND OBJECTIVES: Timely identification of dysarthria progression in patients with bulbar-onset amyotrophic lateral sclerosis (ALS) is relevant to have a comprehensive assessment of the disease evolution. To this goal literature recognized the utmost importance of the assessment of the number of syllables uttered by a subject during the oral diadochokinesis (DDK) test. METHODS: To support clinicians, this work proposes a remote deep learning-based system, which consists (i) of a web application to acquire audio tracks of bulbar-onset ALS patients and healthy control subjects while performing the oral DDK test (i.e., repeating the /pa/, /pa-ta-ka/ and /oo-ee/ syllables) and (ii) a DDK-AID network designed to process the acquired audio signals which have different duration and to output the number of per-task syllables repeated by the subject. RESULTS: The DDK-AID network overcomes the comparative method achieving a mean Accuracy of 90.23 in counting syllables repeated by the eleven bulbar-onset ALS-patients while performing the oral DDK test. CONCLUSIONS: The proposed remote monitoring system, in the light of the achieved performance, represents an important step towards the implementation of self-service telemedicine systems which may ensure customised care plans.

A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets.

The missing data mechanism is a relevant problem in Machine Learning (ML) and biomedical informatics communities. Real-world Electronic Health Record (EHR) datasets comprise several missing values, thus revealing a high level of spatiotemporal sparsity in the predictors' matrix. Several approaches in the state-of-the-art tried to deal with this problem by proposing different data imputation strategies that (i) are often unrelated to the ML model, (ii) are not conceived for EHR data where laboratory exams are not prescribed uniformly over time and percentage of missing values is high (iii) exploit only univariate and linear information on the observed features. Our paper proposes a data imputation strategy based on a clinical conditional Generative Adversarial Network (ccGAN) capable of imputing missing values by exploiting non-linear and multivariate information across patients. Unlike other GAN data imputation-based approaches, our method deals explicitly with the high level of missingness of routine EHR data by conditioning the imputing strategy to the observable values and those fully-annotated. We demonstrated the statistical significance of the ccGAN to other state-of-the-art approaches in terms of imputation (around 19.79% of gain to the best competitor) and predictive performance (up to 1.60% of gain to the best competitor) on a real multi-diabetic centers dataset. We also demonstrated its robustness across different missingness rates (up to 1.61% of gain to the best competitor in the highest missingness rates condition) on an additional benchmark EHR dataset.

A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis.

BACKGROUND AND OBJECTIVES: Patients suffering from neurological diseases may develop dysarthria, a motor speech disorder affecting the execution of speech. Close and quantitative monitoring of dysarthria evolution is crucial for enabling clinicians to promptly implement patients' management strategies and maximizing effectiveness and efficiency of communication functions in term of restoring, compensating or adjusting. In the clinical assessment of orofacial structures and functions, at rest condition or during speech and non-speech movements, a qualitative evaluation is usually performed, throughout visual observation. METHODS: To overcome limitations posed by qualitative assessments, this work presents a store-and-forward self-service telemonitoring system that integrates, within its cloud architecture, a convolutional neural network (CNN) for analyzing video recordings acquired by individuals with dysarthria. This architecture - called facial landmark Mask RCNN - aims at locating facial landmarks as a prior for assessing the orofacial functions related to speech and examining dysarthria evolution in neurological diseases. RESULTS: When tested on the Toronto NeuroFace dataset, a publicly available annotated dataset of video recordings from patients with amyotrophic lateral sclerosis (ALS) and stroke, the proposed CNN achieved a normalized mean error equal to 1.79 on localizing the facial landmarks. We also tested our system in a real-life scenario on 11 bulbar-onset ALS subjects, obtaining promising outcomes in terms of facial landmark position estimation. DISCUSSION AND CONCLUSIONS: This preliminary study represents a relevant step towards the use of remote tools to support clinicians in monitoring the evolution of dysarthria.

Uncovering the potential of innovation ecosystems in the healthcare sector after the COVID-19 crisis.

Industry 4.0 technologies are expected to enhance healthcare quality at the minimum cost feasible by using innovative solutions based on a fruitful exchange of knowledge and resources among institutions, firms and academia. These collaborative mechanisms are likely to occur in an innovation ecosystem where different stakeholders and resources interact to provide ground-breaking solutions to the market. The paper proposes a framework for studying the creation and development of innovation ecosystems in the healthcare sector by using a set of interrelated dimensions including, technology, value, and capabilities within a Triple-Helix model guided by focal actors. The model is applied to an exemplary Italian innovation ecosystem providing cloud and artificial intelligence-based solutions to general practitioners (GPs) under the focal role of the Italian association of GPs. Primary and secondary data are examined starting from the innovation ecosystem's origins and continuing until the COVID-19 crisis. The findings show that the pandemic represented the turning point that altered the ecosystem's dimensions in order to find immediate solutions for monitoring health conditions and organizing the booking of swabs and vaccines. The data triangulation points out the technical, organizational, and administrative barriers hindering the widespread adoption of these solutions at the national and regional levels, revealing several implications for health policy.

TwinEDA: a sustainable deep-learning approach for limb-position estimation in preterm infants' depth images.

Early diagnosis of neurodevelopmental impairments in preterm infants is currently based on the visual analysis of newborns' motion patterns by trained operators. To help automatize this time-consuming and qualitative procedure, we propose a sustainable deep-learning algorithm for accurate limb-pose estimation from depth images. The algorithm consists of a convolutional neural network (TwinEDA) relying on architectural blocks that require limited computation while ensuring high performance in prediction. To ascertain its low computational costs and assess its application in on-the-edge computing, TwinEDA was additionally deployed on a cost-effective single-board computer. The network was validated on a dataset of 27,000 depth video frames collected during the actual clinical practice from 27 preterm infants. When compared to the main state-of-the-art competitor, TwinEDA is twice as fast to predict a single depth frame and four times as light in terms of memory, while performing similarly in terms of Dice similarity coefficient (0.88). This result suggests that the pursuit of efficiency does not imply the detriment of performance. This work is among the first to propose an automatic and sustainable limb-position estimation approach for preterm infants. This represents a significant step towards the development of broadly accessible clinical monitoring applications.

A review on deep-learning algorithms for fetal ultrasound-image analysis.

Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. A number of survey papers in the field is today available, but most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 153 research papers published after 2017. Papers are analyzed and commented from both the methodology and the application perspective. We categorized the papers into (i) fetal standard-plane detection, (ii) anatomical structure analysis and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. In addition, emerging applications are also outlined. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into actual clinical practice.

A deep learning approach to median nerve evaluation in ultrasound images of carpal tunnel inlet.

Ultrasound (US) imaging is recognized as a useful support for Carpal Tunnel Syndrome (CTS) assessment through the evaluation of median nerve morphology. However, US is still far to be systematically adopted to evaluate this common entrapment neuropathy, due to US intrinsic challenges, such as its operator dependency and the lack of standard protocols. To support sonographers, the present study proposes a fully-automatic deep learning approach to median nerve segmentation from US images. We collected and annotated a dataset of 246 images acquired in clinical practice involving 103 rheumatic patients, regardless of anatomical variants (bifid nerve, closed vessels). We developed a Mask R-CNN with two additional transposed layers at segmentation head to accurately segment the median nerve directly on transverse US images. We calculated the cross-sectional area (CSA) of the predicted median nerve. Proposed model achieved good performances both in median nerve detection and segmentation: Precision (Prec), Recall (Rec), Mean Average Precision (mAP) and Dice Similarity Coefficient (DSC) values are 0.916 ± 0.245, 0.938 ± 0.233, 0.936 ± 0.235 and 0.868 ± 0.201, respectively. The CSA values measured on true positive predictions were comparable with the sonographer manual measurements with a mean absolute error (MAE) of 0.918 mm2. Experimental results showed the potential of proposed model, which identified and segmented the median nerve section in normal anatomy images, while still struggling when dealing with infrequent anatomical variants. Future research will expand the dataset including a wider spectrum of normal anatomy and pathology to support sonographers in daily practice.

Generating depth images of preterm infants in given poses using GANs.

BACKGROUND AND OBJECTIVES: The use of deep learning for preterm infant's movement monitoring has the potential to support clinicians in early recognizing motor and behavioural disorders. The development of deep learning algorithms is, however, hampered by the lack of publicly available annotated datasets. METHODS: To mitigate the issue, this paper presents a Generative Adversarial Network-based framework to generate images of preterm infants in a given pose. The framework consists of a bibranch encoder and a conditional Generative Adversarial Network, to generate a rough image and a refined version of it, respectively. RESULTS: Evaluation was performed on the Moving INfants In RGB-D dataset which has 12.000 depth frames from 12 preterm infants. A low Fréchet inception distance (142.9) and an inception score (2.8) close to that of real-image distribution (2.6) are obtained. The results achieved show the potentiality of the framework in generating realistic depth images of preterm infants in a given pose. CONCLUSIONS: Pursuing research on the generation of new data may enable researchers to propose increasingly advanced and effective deep learning-based monitoring systems.

Prediction of complications of type 2 Diabetes: A Machine learning approach.

AIM: To construct predictive models of diabetes complications (DCs) by big data machine learning, based on electronic medical records. METHODS: Six groups of DCs were considered: eye complications, cardiovascular, cerebrovascular, and peripheral vascular disease, nephropathy, diabetic neuropathy. A supervised, tree-based learning approach (XGBoost) was used to predict the onset of each complication within 5 years (task 1). Furthermore, a separate prediction for early (within 2 years) and late (3-5 years) onset of complication (task 2) was performed. A dataset of 147.664 patients seen during 15 years by 23 centers was used. External validation was performed in five additional centers. Models were evaluated by considering accuracy, sensitivity, specificity, and area under the ROC curve (AUC). RESULTS: For all DCs considered, the predictive models in task 1 showed an accuracy > 70 %, and AUC largely exceeded 0.80, reaching 0.97 for nephropathy. For task 2, all predictive models showed an accuracy > 70 % and an AUC > 0.85. Sensitivity in predicting the early occurrence of the complication ranged between 83.2 % (peripheral vascular disease) and 88.5 % (nephropathy). CONCLUSIONS: Machine learning approach offers the opportunity to identify patients at greater risk of complications. This can help overcoming clinical inertia and improving the quality of diabetes care.

Social media analytics system for action inspection on social networks.

Social networks are increasingly used for discussing all kinds of topics, including those related to politics, serving as a virtual arena. Consequently, analysing online conversations, for example, to predict election outcomes, is becoming a popular and challenging research area. On social networking sites, citizens express themselves spontaneously regarding political topics, often driven by specific events in social life. Real-time analysis of social media can provide valuable feedback and insights to both politicians and news agencies. In this paper, we discuss the design and implementation of a system for tracking and analysing social media. The SocMINT system provides an easy-to-use, visual dashboard to monitor the discussion on specific topics, to capture trends in communities and, by iteratively applying multidimensional data analysis and filtering, to deeply analyse posts and influencers. SocMINT aggregates data from multiple social sources and performs sentiment analysis on textual, visual and mixed content via a specifically designed neural network architecture. The system was applied in a real context by administrative staff of a political party to effectively analyse candidates' political communication on Facebook, Instagram and Twitter and the related online community reactions and discussion. In the paper, we report on this real-world case study, showing how the system meaningfully captures trends in public opinion, comparing the main KPIs provided by SocMINT with the outcomes of traditional polls.

Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level.

BACKGROUND: Deep learning applied to ultrasound (US) can provide a feedback to the sonographer about the correct identification of scanned tissues and allows for faster and standardized measurements. The most frequently adopted parameter for US diagnosis of carpal tunnel syndrome is the increasing of the cross-sectional area (CSA) of the median nerve. Our aim was to develop a deep learning algorithm, relying on convolutional neural networks (CNNs), for the localization and segmentation of the median nerve and the automatic measurement of its CSA on US images acquired at the proximal inlet of the carpal tunnel. METHODS: Consecutive patients with rheumatic and musculoskeletal disorders were recruited. Transverse US images were acquired at the carpal tunnel inlet, and the CSA was manually measured. Anatomical variants were registered. The dataset consisted of 246 images (157 for training, 40 for validation, and 49 for testing) from 103 patients each associated with manual annotations of the nerve boundary. A Mask R-CNN, state-of-the-art CNN for image semantic segmentation, was trained on this dataset to accurately localize and segment the median nerve section. To evaluate the performances on the testing set, precision (Prec), recall (Rec), mean average precision (mAP), and Dice similarity coefficient (DSC) were computed. A sub-analysis excluding anatomical variants was performed. The CSA was automatically measured by the algorithm. RESULTS: The algorithm correctly identified the median nerve in 41/49 images (83.7%) and in 41/43 images (95.3%) excluding anatomical variants. The following metrics were obtained (with and without anatomical variants, respectively): Prec 0.86 ± 0.33 and 0.96 ± 0.18, Rec 0.88 ± 0.33 and 0.98 ± 0.15, mAP 0.88 ± 0.33 and 0.98 ± 0.15, and DSC 0.86 ± 0.19 and 0.88 ± 0.19. The agreement between the algorithm and the sonographer CSA measurements was excellent [ICC 0.97 (0.94-0.98)]. CONCLUSIONS: The developed algorithm has shown excellent performances, especially if excluding anatomical variants. Future research should aim at expanding the US image dataset including a wider spectrum of normal anatomy and pathology. This deep learning approach has shown very high potentiality for a fully automatic support for US assessment of carpal tunnel syndrome.

A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign.

The current ML approaches do not fully focus to answer a still unresolved and topical challenge, namely the prediction of priorities of COVID-19 vaccine administration. Thus, our task includes some additional methodological challenges mainly related to avoiding unwanted bias while handling categorical and ordinal data with a highly imbalanced nature. Hence, the main contribution of this study is to propose a machine learning algorithm, namely Hierarchical Priority Classification eXtreme Gradient Boosting for priority classification for COVID-19 vaccine administration using the Italian Federation of General Practitioners dataset that contains Electronic Health Record data of 17k patients. We measured the effectiveness of the proposed methodology for classifying all the priority classes while demonstrating a significant improvement with respect to the state of the art. The proposed ML approach, which is integrated into a clinical decision support system, is currently supporting General Pracitioners in assigning COVID-19 vaccine administration priorities to their assistants.

A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images.

OBJECTIVE: Rheumatoid arthritis (RA) is a chronic disease characterized by erosive symmetrical polyarthritis. Bone and cartilage are the main joint targets of this disease. Cartilage damage is one of the most relevant determinants of physical disability in RA patients. Cartilage damage is nowadays assessed by clinicians, which manually measure cartilage thickness in ultrasound (US) imaging. This poses issues relevant to intra-and inter-observer variability. Relying on the acquisition of metacarpal-head US images from 38 subjects, this work addresses the problem of automatic cartilage-thickness measurement by designing a new deep-learning (DL) framework. METHODS: The framework consists of a Convolutional Neural Network (CNN), responsible for regressing cartilage-interface distance fields, followed by a post-processing step to delineate the two cartilage interfaces from the predicted distance fields and compute the cartilage thickness. RESULTS: Our framework achieved encouraging results with a mean absolute difference (ADF) of 0.032 (±0.026) mm against manual thickness annotation by an expert clinician. When evaluating the intra-observer variability, we obtained an ADF = 0.036 (±0.028) mm. CONCLUSION: The proposed framework achieved an ADF against manual annotation that was comparable to the intra-observer variability, proving the potential of DL in the field. SIGNIFICANCE: This work is the first to address the problem of automatic cartilage-thickness estimation in US rheumatological images with DL, paving the way for future research in the field. From a clinical perspective, the proposed framework proved to be a valuable tool to support the clinical routine increasing the reproducibility of cartilage thickness measurements.

Improving Preterm Infants' Joint Detection in Depth Images Via Dense Convolutional Neural Networks.

Preterm infants' spontaneous motility is a valuable diagnostic and prognostic index of motor and cognitive impairments. Despite being recognized as crucial, preterm infant's movement assessment is mostly based on clinicians' visual inspection. The aim of this work is to present a 2D dense convolutional neural network (denseCNN) to detect preterm infant's joints in depth images acquired in neonatal intensive care units. The denseCNN allows to improve the performance of our previous model in the detection of joints and joint connections, reaching a median recall value equal to 0.839. With a view to monitor preterm infants in a scenario where computational resources are scarce, we tested the architecture on a mid-range laptop. The prediction occurs in real-time (0.014 s per image), opening up the possibility of integrating such monitoring system in a domestic environment.

Asymmetric Three-dimensional Convolutions For Preterm Infants' Pose Estimation.

Computer-assisted tools for preterm infants' movement monitoring in neonatal intensive care unit (NICU) could support clinicians in highlighting preterm-birth complications. With such a view, in this work we propose a deep-learning framework for preterm infants' pose estimation from depth videos acquired in the actual clinical practice. The pipeline consists of two consecutive convolutional neural networks (CNNs). The first CNN (inherited from our previous work) acts to roughly predict joints and joint-connections position, while the second CNN (Asy-regression CNN) refines such predictions to trace the limb pose. Asy-regression relies on asymmetric convolutions to temporally optimize both the training and predictions phase. Compared to its counterpart without asymmetric convolutions, Asy-regression experiences a reduction in training and prediction time of 66% , while keeping the root mean square error, computed against manual pose annotation, merely unchanged. Research mostly works to develop highly accurate models, few efforts have been invested to make the training and deployment of such models time-effective. With a view to make these monitoring technologies sustainable, here we focused on the second aspect and addressed the problem of designing a framework as trade-off between reliability and efficiency.

Learning-Based Median Nerve Segmentation From Ultrasound Images For Carpal Tunnel Syndrome Evaluation.

Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy. Ultrasound imaging (US) may help to diagnose and assess CTS, through the evaluation of median nerve morphology. To support sonographers, this paper proposes a fully-automatic deep-learning approach to median nerve segmentation from US images. The approach relies on Mask R-CNN, a convolutional neural network that is trained end-to-end. The segmentation head of Mask R-CNN is here evaluated with three different configurations, with the goal of studying the effect of the segmentation-head output resolution on the overall Mask R-CNN segmentation performance. For this study, we collected and annotated a dataset of 151 images acquired in the actual clinical practice from 53 subjects with CTS. To our knowledge, this is the largest dataset in the field in terms of subjects. We achieved a median Dice similarity coefficient equal to 0.931 (IQR = 0.027), demonstrating the potentiality of the proposed approach. These results are a promising step towards providing an effective tool for CTS assessment in the actual clinical practice.

The INCREASE project: Intelligent Collections of food-legume genetic resources for European agrofood systems.

Food legumes are crucial for all agriculture-related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co-benefits for human health. The characterization, maintenance and exploitation of food-legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin (Lupinus albus and L. mutabilis), a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single-seed descent-purified accessions (i.e., inbred lines), will be developed, exploiting germplasm available both from genebanks and on-farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agri-food and non-food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources.