Social and functional inclusion of an undergraduate student with unilateral upper limb agenesis: a case study.

INTRODUCTION: The inclusion of health professionals who have some type of disability brings benefits, such as the increase in empathy that facilitates the relationship with patients. In this work, we present a case study on the inclusion of an undergraduate Dentistry student with upper limb agenesis. PURPOSE: In order to improve the quality of life for people with upper limb agenesis, it is urgent to establish inclusion strategies in the educational field. This process included the use of assistive technologies to allow the manipulation of instruments used in the practice of dental clinical activities, enabling future Dentists with upper limb agenesis to have an equal education in relation to their academic peers. CASE SELECTION: The case study presented in this work consists of the follow-up of G., a 23-year-old female Dentistry student. The developed adaptations were made using a low-temperature thermoplastic. The expert team that participated in this process was formed by three occupational therapists and three Dentistry professors. The process included several stages, being the last one the integration, in which training sessions were performed. OUTCOMES: The process was accessed using two main tools: quantitative questionnaires and qualitative interviews. After the proposed intervention, the volunteer presented a gradual evolution in her occupational performance with the use of most adaptations, promoting her functional training. DISCUSSIONS: Most adaptations allowed G. to perform the activities satisfactorily, with the precision inherent to these tasks, even with little training time. CONCLUSIONS: This case study demonstrates the high applicability of the proposed technologies.

In order to improve the quality of life for people with upper limb agenesis, it is urgent to establish inclusion strategies in the educational field, as assessed in this study caseThe inclusion process of Dentistry students can include the use of assistive technology to allow the manipulation of instruments for the practice of clinical activitiesOur study suggests that the assistive adpatations promote a gradual evolution in the students' occupational performance, functional training and motor qualification.

Dental arch definition in computed tomographs using two semi-automatic methods.

Computed tomography is a widely used image examination in dental imaging that provides an accurate location of oral structures and features, including the dental arch, which is an important anatomical feature. This study proposes two new semi-automatic methods for arch definition in CTs, with minimal user effort. This study includes 25 CT examinations. The first method is based on the teeth pulps, and the second one is based on the whole mandible. The methods use thresholding and morphological operations to obtain the arches. The evaluation process includes two different metrics DTW and IoU. For both metrics, the initial results of M1 were very low, but the average performance of M2 can be considered high. The analysis showed that changing the input improves the M1 results substantially. The promising results presented here suggest that these methods can be used as auxiliary tools for the proposed task.

Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks.

Dental caries is an extremely common problem in dentistry that affects a significant part of the population. Approximal caries are especially difficult to identify because their position makes clinical analysis difficult. Radiographic evaluation-more specifically, bitewing images-are mostly used in such cases. However, incorrect interpretations may interfere with the diagnostic process. To aid dentists in caries evaluation, computational methods and tools can be used. In this work, we propose a new method that combines image processing techniques and convolutional neural networks to identify approximal dental caries in bitewing radiographic images and classify them according to lesion severity. For this study, we acquired 112 bitewing radiographs. From these exams, we extracted individual tooth images from each exam, applied a data augmentation process, and used the resulting images to train CNN classification models. The tooth images were previously labeled by experts to denote the defined classes. We evaluated classification models based on the Inception and ResNet architectures using three different learning rates: 0.1, 0.01, and 0.001. The training process included 2000 iterations, and the best results were achieved by the Inception model with a 0.001 learning rate, whose accuracy on the test set was 73.3%. The results can be considered promising and suggest that the proposed method could be used to assist dentists in the evaluation of bitewing images, and the definition of lesion severity and appropriate treatments.

A Computational Study on the Role of Parameters for Identification of Thyroid Nodules by Infrared Images (and Comparison with Real Data).

According to experts and medical literature, healthy thyroids and thyroids containing benign nodules tend to be less inflamed and less active than those with malignant nodules. It seems to be a consensus that malignant nodules have more blood veins and more blood circulation. This may be related to the maintenance of the nodule's heat at a higher level compared with neighboring tissues. If the internal heat modifies the skin radiation, then it could be detected by infrared sensors. The goal of this work is the investigation of the factors that allow this detection, and the possible relation with any pattern referent to nodule malignancy. We aim to consider a wide range of factors, so a great number of numerical simulations of the heat transfer in the region under analysis, based on the Finite Element method, are performed to study the influence of each nodule and patient characteristics on the infrared sensor acquisition. To do so, the protocol for infrared thyroid examination used in our university's hospital is simulated in the numerical study. This protocol presents two phases. In the first one, the body under observation is in steady state. In the second one, it is submitted to thermal stress (transient state). Both are simulated in order to verify if it is possible (by infrared sensors) to identify different behavior referent to malignant nodules. Moreover, when the simulation indicates possible important aspects, patients with and without similar characteristics are examined to confirm such influences. The results show that the tissues between skin and thyroid, as well as the nodule size, have an influence on superficial temperatures. Other thermal parameters of thyroid nodules show little influence on surface infrared emissions, for instance, those related to the vascularization of the nodule. All details of the physical parameters used in the simulations, characteristics of the real nodules and thermal examinations are publicly available, allowing these simulations to be compared with other types of heat transfer solutions and infrared examination protocols. Among the main contributions of this work, we highlight the simulation of the possible range of parameters, and definition of the simulation approach for mapping the used infrared protocol, promoting the investigation of a possible relation between the heat transfer process and the data obtained by infrared acquisitions.

Do Radiographic Assessments of Periodontal Bone Loss Improve with Deep Learning Methods for Enhanced Image Resolution?

Resolution plays an essential role in oral imaging for periodontal disease assessment. Nevertheless, due to limitations in acquisition tools, a considerable number of oral examinations have low resolution, making the evaluation of this kind of lesion difficult. Recently, the use of deep-learning methods for image resolution improvement has seen an increase in the literature. In this work, we performed two studies to evaluate the effects of using different resolution improvement methods (nearest, bilinear, bicubic, Lanczos, SRCNN, and SRGAN). In the first one, specialized dentists visually analyzed the quality of images treated with these techniques. In the second study, we used those methods as different pre-processing steps for inputs of convolutional neural network (CNN) classifiers (Inception and ResNet) and evaluated whether this process leads to better results. The deep-learning methods lead to a substantial improvement in the visual quality of images but do not necessarily promote better classifier performance.

Application of thermography in the diagnostic investigation of thyroid nodules.

Thyroid nodules (TN) are common in the general population, and the clinical importance of diagnosing thyroid nodules is based on excluding the possibility of thyroid cancer, which occurs in 7-15% of cases. The thyroid gland, owing to its superficial location, is easily accessible via thermography, a noninvasive method of recording body temperature that measures infrared radiation emitted by the body surface. Therefore, this study aimed to evaluate the temperature differences between benign and malignant TN by using thermography. We conducted a cross-sectional study where 147 TN were divided into two groups: the first group included 120 benign nodules and the other included 27 malignant nodules. All the nodules were subjected to ultrasound, fine needle aspiration biopsy, and thermography. On analyzing the thermography results, the benign nodules had a higher temperature at the beginning of the thermography evaluation, and the malignant nodules showed a higher temperature in the middle and at the end (Ft). Using the relationships, it was observed that the temperature delta (ΔT), ΔT nodule/ΔT healthy, ΔT nodule minus ΔT healthy, and nodule Ft minus Ft of the healthy region were higher in malignant nodules. The ROC curve analysis of ΔT demonstrated a cutoff point of 2.38°C, with a sensitivity of 0.963 and specificity of 0.992. Malignant nodules have higher temperatures than benign nodules on thermographic evaluation. This finding suggests that thermography can be a useful tool in the diagnosis of thyroid nodules.

Using super-resolution generative adversarial network models and transfer learning to obtain high resolution digital periapical radiographs.

Periapical Radiographs are commonly used to detect several anomalies, like caries, periodontal, and periapical diseases. Even considering that digital imaging systems used nowadays tend to provide high-quality images, external factors, or even system limitations can result in a vast amount of radiographic images with low quality and resolution. Commercial solutions offer tools based on interpolation methods to increase image resolution. However, previous literature shows that these methods may create undesirable effects in the images affecting the diagnosis accuracy. One alternative is using deep learning-based super-resolution methods to achieve better high-resolution images. Nevertheless, the amount of data for training such models is limited, demanding transfer learning approaches. In this work, we propose the use of super-resolution generative adversarial network (SRGAN) models and transfer learning to achieve periapical images with higher quality and resolution. Moreover, we evaluate the influence of using the transfer learning approach and the datasets selected for it in the final generated images. For that, we performed an experiment comparing the performance of the SRGAN models (with and without transfer learning) with other super-resolution methods. Considering Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS), the results of SRGAN models using transfer learning were better on average. This superiority was also verified statistically using the Wilcoxon paired test. In the visual analysis, the high quality achieved by the SRGAN models, in general, is visible, resulting in more defined edges details and fewer blur effects.