Pen-on-paper pain drawing are an easily administered self-reported measure that enables patients to report the spatial distribution of their pain. The digitalization of pain drawings has facilitated the extraction of quantitative metrics, such as pain extent and location. This study aimed to assess the reliability of pen-on-paper pain drawing analysis conducted by an automated pain-spot recognition algorithm using various scanning procedures. One hundred pain drawings, completed by patients experiencing somatic pain, were repeatedly scanned using diverse technologies and devices. Seven datasets were created, enabling reliability assessments including inter-device, inter-scanner, inter-mobile, inter-software, intra- and inter-operator. Subsequently, the automated pain-spot recognition algorithm estimated pain extent and location values for each digitized pain drawing. The relative reliability of pain extent analysis was determined using the intraclass correlation coefficient, while absolute reliability was evaluated through the standard error of measurement and minimum detectable change. The reliability of pain location analysis was computed using the Jaccard similarity index. The reliability analysis of pain extent consistently yielded intraclass correlation coefficient values above .90 for all scanning procedures, with standard error of measurement ranging from .03% to .13% and minimum detectable change from .08% to .38%. The mean Jaccard index scores across all dataset comparisons exceeded .90. The analysis of pen-on-paper pain drawings demonstrated excellent reliability, suggesting that the automated pain-spot recognition algorithm is unaffected by scanning procedures. These findings support the algorithm's applicability in both research and clinical practice.

Background: Understanding the causes and mechanisms underlying musculoskeletal pain is crucial for developing effective treatments and improving patient outcomes. Self-report measures, such as the Pain Drawing Scale, involve individuals rating their level of pain on a scale. In this technique, individuals color the area where they experience pain, and the resulting picture is rated based on the depicted pain intensity. Analyzing pain drawings (PDs) typically involves measuring the size of the pain region. There are several studies focusing on assessing the clinical use of PDs, and now, with the introduction of digital PDs, the usability and reliability of these platforms need validation. Comparative studies between traditional and digital PDs have shown good agreement and reliability. The evolution of PD acquisition over the last 2 decades mirrors the commercialization of digital technologies. However, the pen-on-paper approach seems to be more accepted by patients, but there is currently no standardized method for scanning PDs. Objective: The objective of this study was to evaluate the accuracy of PD analysis performed by a web platform using various digital scanners. The primary goal was to demonstrate that simple and affordable mobile devices can be used to acquire PDs without losing important information. Methods: Two sets of PDs were generated: one with the addition of 216 colored circles and another composed of various red shapes distributed randomly on a frontal view body chart of an adult male. These drawings were then printed in color on A4 sheets, including QR codes at the corners in order to allow automatic alignment, and subsequently scanned using different devices and apps. The scanners used were flatbed scanners of different sizes and prices (professional, portable flatbed, and home printer or scanner), smartphones with varying price ranges, and 6 virtual scanner apps. The acquisitions were made under normal light conditions by the same operator. Results: High-saturation colors, such as red, cyan, magenta, and yellow, were accurately identified by all devices. The percentage error for small, medium, and large pain spots was consistently below 20% for all devices, with smaller values associated with larger areas. In addition, a significant negative correlation was observed between the percentage of error and spot size (R=−0.237; P=.04). The proposed platform proved to be robust and reliable for acquiring paper PDs via a wide range of scanning devices. Conclusions: This study demonstrates that a web platform can accurately analyze PDs acquired through various digital scanners. The findings support the use of simple and cost-effective mobile devices for PD acquisition without compromising the quality of data. Standardizing the scanning process using the proposed platform can contribute to more efficient and consistent PD analysis in clinical and research settings.

The fourth industrial revolution has driven companies of all sizes to embrace digitalization, recognizing the potential of AI technologies for data analysis and real-time decision-making. However, the adoption of AI by manufacturing SMEs faces challenges related to cost, accessibility, and the need for expertise. To address these challenges, this chapter introduces a groundbreaking platform developed as part of the EU H2020 KITT4SME project. The platform aims to democratize the adoption of AI tools by leveraging the “as-a-service” model, making them affordable and readily available for SMEs. It follows a five-step workflow (diagnose–compose–sense–intervene–evolve) to provide tailor-made AI solutions to SMEs. The distinctive functionality of the platform allows for the composition of AI components from a marketplace into a customized service offering for companies, filling a gap in existing AI platforms. The KITT4SME platform has been successfully applied in four use cases within the project and to 18 external demonstrators via Open Calls. This chapter presents one of the internal use cases to showcase the capabilities and benefits of the KITT4SME platform.

The adoption of Cyber Physical System (CPS) technologies at European level is constrained by a still emerging value chain and by the challenging transformation of manufacturing processes and business ecosystems that their deployment requires. This issue becomes even more challenging when the concept of CPS is exploited to propose cyber-physical machines and manufacturing systems, where the complexity of the controlling intelligence and of the digital counterpart explodes. As a matter of fact, the market behind CPS has a potential that is still scarcely supported by methodologies and tools able to foster the rise of a solid ecosystem required for a relevant market uptake. Multi-sided platforms (MSPs) have demonstrated to play the pivotal role of providing the environments and the technological infrastructures able to match make the needs of manifold user insisting on them. The manufacturing sector did not remain untouched by this trend and moves its first step towards the integration of platform logics across value networks: the CPS business ecosystem is one of those.

To guide a decision-making process oriented to design and produce more sustainable and circular products and components, a comprehensive sustainability assessment and advisory framework was developed and customized for each pilot project within the CIRCUITS project. To ensure it addressed the specific needs of each case (ECU (re)design, design of tire sensors, flexible electronics, and PCB