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

Estimating the severity of dental and oral problems via sentiment classification over clinical reports