Convolutional neural network misclassification analysis in oral lesions: an error evaluation criterion by image characteristics

OBJECTIVE

This retrospective study analyzed the errors generated by a convolutional neural network (CNN) when performing automated classification of oral lesions according to their clinical characteristics, seeking to identify patterns in systemic errors in the intermediate layers of the CNN.

STUDY DESIGN

A cross-sectional analysis nested in a previous trial in which automated classification by a CNN model of elementary lesions from clinical images of oral lesions was performed. The resulting CNN classification errors formed the dataset for this study. A total of 116 real outputs were identified that diverged from the estimated outputs, representing 7.6% of the total images analyzed by the CNN.

RESULTS

The discrepancies between the real and estimated outputs were associated with problems relating to image sharpness, resolution, and focus; human errors; and the impact of data augmentation.

CONCLUSIONS

From qualitative analysis of errors in the process of automated classification of clinical images, it was possible to confirm the impact of image quality, as well as identify the strong impact of the data augmentation process. Knowledge of the factors that models evaluate to make decisions can increase confidence in the high classification potential of CNNs.

Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine-A Systematic Review

Artificial neural networks (ANN) are artificial intelligence (AI) techniques used in the automated recognition and classification of pathological changes from clinical images in areas such as ophthalmology, dermatology, and oral medicine. The combination of enterprise imaging and AI is gaining notoriety for its potential benefits in healthcare areas such as cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, and endoscopic. The present study aimed to analyze, through a systematic literature review, the application of performance of ANN and deep learning in the recognition and automated classification of lesions from clinical images, when comparing to the human performance. The PRISMA 2020 approach (Preferred Reporting Items for Systematic Reviews and Meta-analyses) was used by searching four databases of studies that reference the use of IA to define the diagnosis of lesions in ophthalmology, dermatology, and oral medicine areas. A quantitative and qualitative analyses of the articles that met the inclusion criteria were performed. The search yielded the inclusion of 60 studies. It was found that the interest in the topic has increased, especially in the last 3 years. We observed that the performance of IA models is promising, with high accuracy, sensitivity, and specificity, most of them had outcomes equivalent to human comparators. The reproducibility of the performance of models in real-life practice has been reported as a critical point. Study designs and results have been progressively improved. IA resources have the potential to contribute to several areas of health. In the coming years, it is likely to be incorporated into everyday life, contributing to the precision and reducing the time required by the diagnostic process.

Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images

OBJECTIVES

Artificial intelligence has generated a significant impact in the health field. The aim of this study was to perform the training and validation of a convolutional neural network (CNN)-based model to automatically classify six clinical representation categories of oral lesion images.

METHOD

The CNN model was developed with the objective of automatically classifying the images into six categories of elementary lesions: (1) papule/nodule; (2) macule/spot; (3) vesicle/bullous; (4) erosion; (5) ulcer and (6) plaque. We selected four architectures and using our dataset we decided to test the following architectures: ResNet-50, VGG16, InceptionV3 and Xception. We used the confusion matrix as the main metric for the CNN evaluation and discussion.

RESULTS

A total of 5069 images of oral mucosa lesions were used. The oral elementary lesions classification reached the best result using an architecture based on InceptionV3. After hyperparameter optimization, we reached more than 71% correct predictions in all six lesion classes. The classification achieved an average accuracy of 95.09% in our dataset.

CONCLUSIONS

We reported the development of an artificial intelligence model for the automated classification of elementary lesions from oral clinical images, achieving satisfactory performance. Future directions include the study of including trained layers to establish patterns of characteristics that determine benign, potentially malignant and malignant lesions.

Generalized gingival necrosis in a patient under methimazole use

Hyperthyroidism is a common disease, with a prevalence between 0.2% and 0.5%, characterized by an increase in the synthesis and release of thyroid hormones. Management of this condition requires medical intervention to correct the hormonal imbalance and control its clinical manifestations. Methimazole is a thionamide derivative considered among the first-choice treatment options for hyperthyroidism. However, it may cause serious side effects such as neutropenia or agranulocytosis, which, although rare, can lead to death. The clinical manifestations of this complication range from fever, ulcerations in the oral and pharyngeal mucosa, tonsillitis, and lymphadenopathy to hemorrhagic necrosis and septicemia. This report describes the case of a patient with generalized gingival necrosis that was related to the use of methimazole for the treatment of hyperthyroidism.

The Stomatological Complications of Diamond-Blackfan Anemia: A Case Report

Diamond-Blackfan Anemia (DBA) is a rare heterogeneous genetic disease characterized by severe anemia, reduction or absence of erythroid progenitors, and pro-apoptoptic hematopoiesis, which culminates in bone marrow failure. The disease generally manifests in infancy, as craniofacial, cardiac, genitourinary, and upper limb congenital anomalies. Therapy with corticoids is the treatment of choice, while blood transfusion is adopted during diagnosis and as a chronic approach if the patient does not respond to corticoids. This case report describes DBA in a patient that presented with lesions on the oral mucosa caused by secondary neutropenia. The stomatologist plays an important role in a transdisciplinary team and must remain attentive to the general health conditions of patients, since some oral lesions may be associated with systemic events.