  Analyzing authors' sentiments in texts as a technique for identifying text
polarity can be practical and useful in various fields, including medicine and
dentistry. Currently, due to factors such as patients' limited knowledge about
their condition, difficulties in accessing specialist doctors, or fear of
illness, particularly in pandemic conditions, there might be a delay between
receiving a radiology report and consulting a doctor. In some cases, this delay
can pose significant risks to the patient, making timely decision-making
crucial. Having an automatic system that can inform patients about the
deterioration of their condition by analyzing the text of radiology reports
could greatly impact timely decision-making. In this study, a dataset
comprising 1,134 cone-beam computed tomography (CBCT) photo reports was
collected from the Shiraz University of Medical Sciences. Each case was
examined, and an expert labeled a severity level for the patient's condition on
each document. After preprocessing all the text data, a deep learning model
based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)
network architecture, known as CNN-LSTM, was developed to detect the severity
level of the patient's problem based on sentiment analysis in the radiologist's
report. The model's performance was evaluated on two datasets, each with two
and four classes, in both imbalanced and balanced scenarios. Finally, to
demonstrate the effectiveness of our model, we compared its performance with
that of other classification models. The results, along with one-way ANOVA and
Tukey's test, indicated that our proposed model (CNN-LSTM) performed the best
according to precision, recall, and f-measure criteria. This suggests that it
can be a reliable model for estimating the severity of oral and dental
diseases, thereby assisting patients.
