Regenerative medicine combines elements of tissue engineering and molecular biology aiming to support the regeneration and repair processes of damaged tissues, cells and organs. The most commonly used preparation in regenerative medicine is platelet rich plasma (PRP) containing numerous growth factors present in platelet granularities. This therapy is increasingly used in various fields of medicine. This article is a review of literature on the use of PRP in gynecology and obstetrics. There is no doubt that the released growth factors and proteins have a beneficial effect on wound healing and regeneration processes. So far, its widest application is in reproductive medicine, especially in cases of thin endometrium, Asherman’s syndrome, or premature ovarian failure (POF) but also in wound healing and lower urinary tract symptoms (LUTS), such as urinary incontinence or recurrent genitourinary fistula auxiliary treatment. Further research is, however, needed to confirm the effectiveness and the possibility of its application in many other disorders.

Purpose:The aim of this study was to evaluate the frequency of apical root resorption in the anterior teeth of the maxilla visible on panoramic images during orthodontic treatment with a fixed appliance. Material and methods:A total of 194 panoramic radiographs of patients with a fixed appliance in the upper arch were analysed to evaluate the severity of root resorption in maxillary incisors and canines according to Levander and Malmgren classification. The research group included 135 females and 59 males, aged 15-28 years, with a mean 20.6 years. Results:Of examined patients 75.26% had signs of apical root resorption. The tooth most frequently affected by resorptive changes was the right central upper incisor. The gender and age of the patients were not found to be significant factors. The highest number of teeth had second (II) stage root resorption (53.09%). Conclusions:Panoramic radiographs can be useful in diagnosing external apical root resorption due to orthodontic treatment.

Bite-wing radiographs are one of the most used intraoral radiography techniques in dentistry. AI is extremely important in terms of more efficient patient care in the field of dentistry. The aim of this study was to perform a diagnostic evaluation on bite-wing radiographs with an AI model based on CNNs. In this study, 500 bite-wing radiographs in the radiography archive of Eskişehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology were used. The CranioCatch labeling program (CranioCatch, Eskisehir, Turkey) with tooth decays, crowns, pulp, restoration material, and root-filling material for five different diagnoses were made by labeling the segmentation technique. The U-Net architecture was used to develop the AI model. F1 score, sensitivity, and precision results of the study, respectively, caries 0.8818–0.8235–0.9491, crown; 0.9629–0.9285–1, pulp; 0.9631–0.9843–0.9429, with restoration material; and 0.9714–0.9622–0.9807 was obtained as 0.9722–0.9459–1 for the root filling material. This study has shown that an AI model can be used to automatically evaluate bite-wing radiographs and the results are promising. Owing to these automatically prepared charts, physicians in a clinical intense tempo will be able to work more efficiently and quickly.

While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.

Dental caries is a very common condition, which can lead to serious complications, including tooth loss and infection of the whole human body. Dentists in their daily practice, apart from visual-tactile examination, use radiological methods, such as periapical radiographs and bitewings. Artificial intelligence (AI) is a tool that can be used in diagnosing and detecting cavities. It can help to avoid more invasive treatment and further consequences. The goal of this systematic review was to present the use of artificial intelligence in radiological dental caries diagnostics. In total, twelve studies meeting inclusion criteria were analyzed, and image databases varied from 93 to 3,868 radiographs, with average value of 1,091.17 radiographs. Most of the included studies employed bitewings and periapical images, and authors used different methods and AI algorithms. Accuracy was performed in nine researches. The highest accuracy was 99%, the lowest 73.3%. Also, nine researches provided information on number of observers, which varied from 1 to 25. Comparing all the studies, it was difficult to draw out a conclusion. Artificial intelligence in radiological images may assist dentists and radiologist to perform better and faster examination, and it may be a used in routine dental care. However, more researches are needed in the field of dentistry and radiology.