Advancements in technology have always had major impacts on medicine. Increased use of smartphone and related software applications has created a new era in clinical data exchange among patients and clinicians. Teledentistry is a combination of telecommunications and dentistry, involving the exchange of clinical information and images over remote distances for dental consultation and treatment planning. It has the potential to address many of the problems related to access, cost efficiency and quality of dental care. Through teleconsultation with specialists in larger communities, a dentist in a nearby community can provide access to specialty care for their patients easily. Teledentistry can extend care to underserved patient populations, such as those in rural areas, at a reasonable cost. This review article aims to emphasize the importance of teledentistry in various specialties of dentistry and its role in serving the underserved population.

Background and Objectives: Nowadays, more and more open source medical imaging databases are published on the Internet for medical teaching, algorithm development, and medical research. However, a statistical survey of these databases is still lacking. In this survey, we summarize the current status of open source medical image databases on the Internet. The aim is to make it easier for everyone to find and use open source medical image data. Methods: Information about publicly available medical image databases was collected by searching for scientific papers and Internet search engines. Based on the collected information, the number of databases and the number of images were counted for different diseases, body parts, imaging modalities, and countries. Results: Cancer, particularly breast cancer and lung cancer, ranked top in database numbers among all diseases. The breast, brain, lung, and chest are the top four body parts in terms of database numbers. Computed tomography, magnetic resonance imaging, and X-ray are the most common imaging modalities in the open source datasets. The USA and the Netherlands are the top two countries who own the most databases. Conclusions: The rankings for diseases and body parts were closely related to the diseases morbidity and the health-care expenditure of a country. The number of open sources of medical imaging databases is still growing; there is a need for continuous statistical research on their existence status in the coming years. The list of all the collected databases is opened on the Internet (https://docs.qq.com/sheet/DQWF0QlZKVHpHU1Za).

Background and Objectives: This study investigates a novel type of textile pressure sensor fabricated in a single production step. The work characterizes two designs of electronic textile pressure sensor creating new knowledge into the operation of these types of textile sensors. Interest in electronic flexible film and electronic textile pressure sensing has grown in recent years given their potential in medical applications, principally in developing monitoring solutions for wheelchair users and hospital patients to help prevent the formation of pressure ulcers. Materials and Methods: Two designs of textile pressure sensor were produced using computerized flat-bed knitting. One design was produced in a single step, where the conductive tracks were incorporated into the top and bottom surfaces of a knitted spacer structure (knitted spacer pressure sensor). The other sensor was comprised of separate knitted layers. The response of the sensors was tested by changing the applied pressure in two ways: By altering the applied force or changing the area of the applied force. Sensor hysteresis and how the sensor thickness affected its response were also examined. Results: The two sensor designs behaved differently under the tested conditions. The knitted spacer pressure sensor was pressure sensitive up to 25 kPa and showed no hysteretic effects over the pressure range of interest. Conclusions: This study presents a fully textile pressure sensor that was produced with a single production step and demonstrates its functionality over the pressure range of interest for monitoring wheelchair users.

Background and Objectives: Developing serious games (SGs) for children is challenging, especially when dealing with complex medical diagnosis. Enhancing children's compliance for the treatment of chronic conditions is a crucial challenge that requires caring about the engagement of users in the game experience already from the initial stages of the development. Materials and Methods: Participatory design is the methodological key to trace the right path toward an effective and easy-to-use game; specific methodological settings are necessary to collect meaningful feedback and guide the creation of the game. Our article reports the involvement of 14 young users in two different stages of the design and development of two SGs for chronic clinical conditions (Crohn's disease and cystic fibrosis). Results: Specific feedbacks were reported about game contents (e.g., the preference for anthropomorphic avatars) and technological issues (e.g., the need of a graphical tutorial). Conclusions: Using the same methodological approach in two different phases of the development allows to highlight children's perspective toward a technological solution addressing clinical compliance.

Background and Objectives: Multiple sclerosis (MS) lesion segmentation is important in estimating the progress of the disease and measuring the impact of new clinical treatments. Manual lesion delineation for the segmentation of lesions is time-consuming and suffers from observer variability. Therefore, a fully automated MS lesion segmentation method is considerable important in clinical practice. Subjects and Methods: In this study, we present a multilabel fusion embedded level set method for white matter lesion segmentation from MS patient images. Specifically, we focus on the validation of the variational level set method. Lesion segmentation is achieved by extending the level set contour which consists of an intensity-constrained term, an image data term, and a regularization term. Results: To compare the performance of our method with other state-of-the-art methods, we evaluated the methods with 25 magnetic resonance imaging datasets of MS patients. The dice score reaches an average of 0.55 for the proposed method. The sensitivity value and specificity value reach an average of 0.89 and 0.14, respectively. Conclusions: Experimental results demonstrate that our method is robust to parameter setting and outperforms other methods. The intensity-constrained term plays a key role in improving the segmentation accuracy. The experimental results show that our approach is effective and robust for lesion segmentation, which might simplify the quantification of lesions in basic research and even clinical trials.