Sickle cell toothache

Sickle cell disease classification using deep learning

This paper presents a transfer and deep learning based approach to the classification of Sickle Cell Disease (SCD). Five transfer learning models such as ResNet-50, AlexNet, MobileNet, VGG-16 and VGG-19, and a sequential convolutional neural network (CNN) have been implemented for SCD classification. ErythrocytesIDB dataset has been used for training and testing the models. In order to make up for the data insufficiency of the erythrocytesIDB dataset, advanced image augmentation techniques are employed to ensure the robustness of the dataset, enhance dataset diversity and improve the accuracy of the models. An ablation experiment using Random Forest and Support Vector Machine (SVM) classifiers along with various hyperparameter tweaking was carried out to determine the contribution of different model elements on their predicted accuracy. A rigorous statistical analysis was carried out for evaluation and to further evaluate the model's robustness, an adversarial attack test was conducted. The experimental results demonstrate compelling performance across all models. After performing the statistical tests, it was observed that MobileNet showed a significant improvement (p = 0.0229), while other models (ResNet-50, AlexNet, VGG-16, VGG-19) did not (p > 0.05). Notably, the ResNet-50 model achieves remarkable precision, recall, and F1-score values of 100 % for circular, elongated, and other cell shapes when experimented with a smaller dataset. The AlexNet model achieves a balanced precision (98 %) and recall (99 %) for circular and elongated shapes. Meanwhile, the other models showcase competitive performance.

Orofacial manifestations of sickle cell disease: implications for dental clinicians

Sickle cell disease is a multisystem disease associated with episodes of acute illness and progressive organ damage. It is one of the most common severe monogenic disorders worldwide and is now the fastest growing genetic disorder in the UK. Dental awareness of orofacial manifestation varies geographically due to the inheritance pattern and patient population affected by the disease. This article details the epidemiology, pathophysiology and the orofacial manifestations in light of current literature.

The influence of systemic diseases on the diagnosis of oral diseases: a problem-based approach

Although all dentists are taught about the importance of oral health to general health and that systemic disease can manifest in the oral cavity, the 4-year dental school curriculum does not allow time to gain competency in these relationships. Nevertheless, all dentists must have skills in taking a medical history and an appreciation of oral findings that might have a systemic origin. This article focuses on the identification of abnormal signs and symptoms in the oral cavity and the determination of those that have a systemic origin. It is imperative that clinicians are mindful of the possible oral-systemic associations, because these could potentially have a huge impact on patient care.