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Pain assessment is essential in developing optimal pain management protocols to alleviate suffering and prevent functional decline in patients. Consequently, reliable and accurate automatic pain assessment systems are essential for continuous and effective patient monitoring. This study presents synthetic thermal videos generated by Generative Adversarial Networks integrated into the pain recognition pipeline and evaluates their efficacy. A framework consisting of a Vision-MLP and a Transformer-based module is utilized, employing RGB and synthetic thermal videos in unimodal and multimodal settings. Experiments conducted on facial videos from the BioVid database demonstrate the effectiveness of synthetic thermal videos and underline the potential advantages of it.

Automatic pain assessment plays a critical role for advancing healthcare and optimizing pain management strategies. This study has been submitted to the First Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4PAIN). The proposed multimodal framework utilizes facial videos and fNIRS and presents a modality-agnostic approach, alleviating the need for domain-specific models. Employing a dual ViT configuration and adopting waveform representations for the fNIRS, as well as for the extracted embeddings from the two modalities, demonstrate the efficacy of the proposed method, achieving an accuracy of 46.76% in the multilevel pain assessment task.

Pain is a complex phenomenon which is manifested and expressed by patients in various forms. The immediate and objective recognition of it is a great of importance in order to attain a reliable and unbiased healthcare system. In this work, we elaborate electrocardiography signals revealing the existence of variations in pain perception among different demographic groups. We exploit this insight by introducing a novel multi-task neural network for automatic pain estimation utilizing the age and the gender information of each individual, and show its advantages compared to other approaches.

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

During the preceding decades, human gait analysis has been the center of attention for the scientific community, while the association between gait analysis and overall health monitoring has been extensively reported. Technological advances further assisted in this alignment, resulting in access to inexpensive and remote healthcare services. Various assessment tools, such as software platforms and mobile applications, have been proposed by the scientific community and the market that employ sensors to monitor human gait for various purposes ranging from biomechanics to the progression of functional recovery. The framework presented herein offers a valuable digital biomarker for diagnosing and monitoring Parkinson's disease that can help clinical experts in the decision-making process leading to corrective planning or patient-specific treatment. More accurate and reliable decisions can be provided through a wide variety of integrated Artificial Intelligence algorithms and straightforward visualization techniques, including, but not limited to, heatmaps and bar plots. The framework consists of three core components: the insole pair, the mobile application, and the cloud-based platform. The insole pair deploys 16 plantar pressure sensors, an accelerometer, and a gyroscope to acquire gait data. The mobile application formulates the data for the cloud platform, which orchestrates the component interaction through the web application. Utilizing open communication protocols enables the straightforward replacement of one of the core components with a relative one (e.g., a different model of insoles), transparently from the end user, without affecting the overall architecture, resulting in a framework with the flexibility to adjust its modularity., Comment: 8 pages, 9 figures

The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Addressing these concerns and risks, the FUTURE-AI framework has been proposed, which, sourced from a global multi-domain expert consensus, comprises guiding principles for increased trust, safety, and adoption for AI in healthcare. In this paper, we transform the general FUTURE-AI healthcare principles to a concise and specific AI implementation guide tailored to the needs of the medical imaging community. To this end, we carefully assess each building block of the FUTURE-AI framework consisting of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability, and respectively define concrete best practices based on accumulated AI implementation experiences from five large European projects on AI in Health Imaging. We accompany our concrete step-by-step medical imaging development guide with a practical AI solution maturity checklist, thus enabling AI development teams to design, evaluate, maintain, and deploy technically, clinically and ethically trustworthy imaging AI solutions into clinical practice., Comment: Please refer to arXiv:2309.12325 for the latest FUTURE-AI framework for healthcare

Prostate segmentation from magnetic resonance imaging (MRI) is a challenging task. In recent years, several network architectures have been proposed to automate this process and alleviate the burden of manual annotation. Although the performance of these models has achieved promising results, there is still room for improvement before these models can be used safely and effectively in clinical practice. One of the major challenges in prostate MR image segmentation is the presence of class imbalance in the image labels where the background pixels dominate over the prostate. In the present work we propose a DL-based pipeline for cropping the region around the prostate from MRI images to produce a more balanced distribution of the foreground pixels (prostate) and the background pixels and improve segmentation accuracy. The effect of DL-cropping for improving the segmentation performance compared to standard center-cropping is assessed using five popular DL networks for prostate segmentation, namely U-net, U-net+, Res Unet++, Bridge U-net and Dense U-net. The proposed smart-cropping outperformed the standard center cropping in terms of segmentation accuracy for all the evaluated prostate segmentation networks. In terms of Dice score, the highest improvement was achieved for the U-net+ and ResU-net++ architectures corresponding to 8.9% and 8%, respectively., Comment: 8 pages, 6 figures, 1 table