Virtual Reality Animal Rescue World: Pediatric virtual reality analgesia during just noticeable pressure pain in children aged 2-10 years old (crossover design).

Background and aims: Excessive pain during medical procedures is a worldwide medical problem. Most scald burns occur in children under 6, who are often undermedicated. Adjunctive Virtual Reality (VR) distraction has been shown to reduce pain in children aged 6-17, but little is known about VR analgesia in young children. This study tests whether desktop VR (VR Animal Rescue World) can reduce the just noticeable pressure pain of children aged 2-10. Methods: A within-subject repeated measures design was used. With treatment order randomized, each healthy volunteer pediatric participant underwent brief cutaneous pressure stimuli under three conditions: (1) no distraction, (2) a verbal color naming task (no VR), and (3) a large TV-based desktop VR distraction. A hand-held Wagner pressure pain stimulation device was used to generate just noticeable pain sensations. Participants indicated when a steadily increasing non-painful pressure stimulus first turned into a painful pressure sensation (just noticeable pain). Results: A total of 40 healthy children participated (43% aged 2-5 years; and 57% aged 6-10 years). Compared to the no distraction condition, the 40 children showed significant VR analgesia (i.e., a significant reduction in pain sensitivity during the VR Animal Rescue World condition), t(39) = 9.83, p < 0.001, SD = 6.24. VR was also significantly more effective at reducing pain sensitivity vs. an auditory color naming task, t(39) = 5.42, p < 0.001, SD = 5.94. The subset of children aged 2-5 showed significant reductions in pain during VR. Children under 6 showed greater sensitivity to pain during no distraction than children aged 6-10. Conclusion: During no distraction, children under 6 years old were significantly more sensitive to pain than children aged 6-10. Virtual reality (VR) significantly reduced the "just noticeable" pressure pain sensitivity of children in both age groups.

An MRI Scans-Based Alzheimer's Disease Detection via Convolutional Neural Network and Transfer Learning.

Alzheimer's disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85−3.88%, 2.43−2.66%, and 1.8−40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively.