Advancing Wound Filling Extraction on 3D Faces: A Auto-Segmentation and Wound Face Regeneration Approach

Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions. To achieve accurate segmentation, we conducted thorough experiments and selected a high-performing model from the trained models. The selected model demonstrates exceptional segmentation performance for complex 3D facial wounds. Furthermore, based on the segmentation model, we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study. Our method achieved a remarkable accuracy of 0.9999986\% on the test suite, surpassing the performance of the previous method. From this result, we use 3D printing technology to illustrate the shape of the wound filling. The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design. By automating facial wound segmentation and improving the accuracy of wound-filling extraction, our approach can assist in carefully assessing and optimizing interventions, leading to enhanced patient outcomes. Additionally, it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants. Our source code is available at \url{https://github.com/SIMOGroup/WoundFilling3D}

Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures

In this study, we emphasize the integration of a pre-trained MICA model with an imperfect face dataset, employing a self-supervised learning approach. We present an innovative method for regenerating flawed facial structures, yielding 3D printable outputs that effectively support physicians in their patient treatment process. Our results highlight the model's capacity for concealing scars and achieving comprehensive facial reconstructions without discernible scarring. By capitalizing on pre-trained models and necessitating only a few hours of supplementary training, our methodology adeptly devises an optimal model for reconstructing damaged and imperfect facial features. Harnessing contemporary 3D printing technology, we institute a standardized protocol for fabricating realistic, camouflaging mask models for patients in a laboratory environment

Sensorimotor representation learning for an "active self" in robots: A model survey

Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyse what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration

Prevalence and Determinants of Medication Adherence among Patients with HIV/AIDS in Southern Vietnam

This study was conducted to determine the prevalence and determinants of medication adherence among patients with HIV/AIDS in southern Vietnam. METHODS: A cross-sectional study was conducted in a hospital in southern Vietnam from June to December 2019 on patients who began antiretroviral therapy (ART) for at least 6 months. Using a designed questionnaire, patients were considered adherent if they took correct medicines with right doses, on time and properly with food and beverage and had follow-up visits as scheduled. Multivariable logistic regression was used to identify determinants of adherence. KEY FINDINGS: A total of 350 patients (from 861 medical records) were eligible for the study. The majority of patients were male (62.9%), and the dominant age group (≥35 years old) accounted for 53.7% of patients. Sexual intercourse was the primary route of transmission of HIV (95.1%). The proportions of participants who took the correct medicine and at a proper dose were 98.3% and 86.3%, respectively. In total, 94.9% of participants took medicine appropriately in combination with food and beverage, and 75.7% of participants were strictly adherent to ART. The factors marital status (odds ratio (OR) = 2.54; 95%CI = 1.51-4.28), being away from home (OR = 1.7; 95%CI = 1.03-2.78), substance abuse (OR = 2.7; 95%CI = 1.44-5.05), general knowledge about ART (OR = 2.75; 95%CI = 1.67-4.53), stopping medication after improvement (OR = 4.16; 95%CI = 2.29-7.56) and self-assessment of therapy adherence (OR = 9.83; 95%CI = 5.44-17.77) were significantly associated with patients' adherence. CONCLUSIONS: Three-quarters of patients were adherent to ART. Researchers should consider these determinants of adherence in developing interventions in further studies