Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

The possibility to realize highly customized orthoses is receiving boost thanks to the widespread diffusion of low-cost 3D printing technologies. However, rapid prototyping (RP) with 3D printers is only the final stage of patient personalized orthotics processes. A reverse engineering (RE) process is in fact essential before RP, to digitize the 3D anatomy of interest and to process the obtained surface with suitable modeling software, in order to produce the virtual solid model of the orthosis to be printed. In this paper, we focus on the specific and demanding case of the customized production of hand orthosis. We design and test the essential steps of the entire production process with particular emphasis on the accurate acquisition of the forearm geometry and on the subsequent production of a printable model of the orthosis. The choice of the various hardware and software tools (3D scanner, modeling software, and FDM printer) is aimed at the mitigation of the design and production costs while guaranteeing suitable levels of data accuracy, process efficiency, and design versatility. Eventually, the proposed method is critically analyzed so that the residual issues and critical aspects are highlighted in order to discuss possible alternative approaches and to derive insightful observations that could guide future research activities.

Background: Remote digital monitoring during orthodontic treatment can help patients in improving their oral hygiene performance and reducing the number of appointments due to emergency reasons, especially in time of COVID-19 pandemic where non-urgent appointments might be discouraged. Methods: Thirty patients scheduled to start an orthodontic treatment were divided into two groups of fifteen. Compared to controls, study group patients were provided with scan box and cheek retractor (Dental Monitoring®) and were instructed to take monthly intra-oral scans. Plaque Index (PI), Gingival Index (GI), and White Spot Lesions (WSL) were recorded for both groups at baseline (t₀), every month for the first 3 months (t₁, t₂, t₃), and at 6 months (t₄). Carious Lesions Onset (CLO) and Emergency Appointments (EA) were also recorded during the observation period. Inter-group differences were assessed with Student's t test and Chi-square test, intra-group differences were assessed with Cochran’s Q-test (significance α = 0.05). Results: Study group patients showed a significant improvement in plaque control at t₃ (p = 0.010) and t₄ (p = 0.039), compared to control group. No significant difference was observed in the number of WSL between the two groups. No cavities were detected in the study group, while five CLO were diagnosed in the control group (p = 0.049). A decreased number of EA was observed in the study group, but the difference was not significant. Conclusions: Integration of a remote monitoring system during orthodontic treatment was effective in improving plaque control and reducing carious lesions onset. The present findings encourage orthodontists to consider this technology to help maintaining optimal oral health of patients, especially in times of health emergency crisis.

Full Laboratory Automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we callDeepColony. Working on a large stream of clinical data and a complete set of 32 pathogens, the proposed system is capable of effectively assist plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of Urinary Tract Infections. Moreover, thanks to the rich species-related generated information,DeepColonycan be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.

In the field of rehabilitation, the 3D scanning of body parts can be considered a crucial starting point for subsequent 3D model design of customised devices, especially when additive manufacturing techniques are involved in their production. This study experimentally evaluates and identifies appropriate procedures to acquire and process 3D anatomic images of the hand, including fingers, for the design of customised orthoses. Hand scanning is a complex activity and requires solutions capable of solving problematic aspects, such as the difficulty in maintaining the hand in a steady position and the presence of motion artefacts due to involuntary movements. We addressed such issues by considering the use of two different kinds of optical scanning device. The acquisition process has been initially defined based on healthy subjects and then extended to patients affected by pathologies that compromise upper limb functionality. Quality anatomical models were produced thanks to the application of advanced geometry processing technologies for the automated alignment of multiple scans and the removal of artefacts due to involuntary movements. As a result, with distinctive pros and cons, both the proposed combinations of scanning procedures and dedicated geometry processing evidenced their suitability in producing complete and accurate enough 3D models to be exploited for the subsequent design and production of customised hand orthoses in a typical reverse engineering pipeline.