Karolina Futyma-Gąbka’s research while affiliated with Medical University of Lublin and other places

This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra-and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as 'presence of caries' and 13,928 surfaces are determined as 'absence of caries' for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images.

This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, USA) for caries detection, by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. 6008 surfaces are determined as ‘presence of caries’ and 13928 surfaces are determined as ‘absence of caries’ for ground truth. The area under the ROC curve of Observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468 and the best accuracy (0.939) is achieved in the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detecting of dental caries with CBCT images.

Background: The normal anatomy of mandibular canines presents with 1 root and 1 root canal. Two roots are found in approx. 2% of cases, and a bilateral configuration is even rarer. Canines with 2 root canals are found in around 15% of cases. Cone-beam computed tomography (CBCT) enables the detailed visualization of the teeth. Objectives: The present study aimed to evaluate the prevalence of two-rooted mandibular canines and one-rooted mandibular canines with 2 root canals in a Polish population by using CBCT. Material and methods: A total of 300 consecutive CBCT scans, taken for different clinical indications, were examined to assess permanent mandibular canine anatomy. The study group included 182 females and 118 males aged 12-86 years (mean age: 31.7 years). Results: Among 600 cases, 27 two-rooted teeth were found (4.5%), and there were only 6 cases of onerooted mandibular canines with 2 root canals (1.0%). Six cases of two-rooted canines had this configuration bilaterally, all in females. Five cases of canines with 2 root canals were found on the left side (83.3%). The predominance of the occurrence of two-rooted canines in females (81.5%) was strongly emphasized. Conclusions: The prevalence of two-rooted mandibular canines in a Polish population, evaluated by means of CBCT, was higher, while the presence of 2 root canals was lower than in recent literature reports. There was no side predilection of two-rooted mandibular canines, although their occurrence was higher in females.

The aim of the study is to present two examples of twins with differences in their dentition revealed with the use of panoramic examinations. The analysis based on panoramic X-rays shows that even though the monozygotic twins share 100% of the genome, their dentitions reveal significant differences.

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.

Progressive systemic sclerosis (PSS) is chronic autoimmune disease affecting a connective tissue. The symptoms of PSS in orofacial area are: restricted mouth opening, xerostomia, facial asymmetry and problems with oral hygiene. Radiographic images can show specific features like bone resorption, especially in mandibular region or periodontal ligament space widening. The aim of this study was to present the case of 56-year-old woman with characteristic scleroderma-related changes visible on panoramic radiograph. The patient diagnosed with severe systemic sclerosis was referred by dermatologist to general dentist. The woman experienced tightening of facial skin, xerostomia and reduced mouth opening which caused problems with daily oral hygiene and dental treatment. General dentist referred the patient to the Department of Dental and Maxillofacial Radiodiagnostics of Medical University of Lublin for the panoramic X-ray. One of the main findings was bilateral resorption of mandibular angles. Localization of the bone resorption in patients with scleroderma is related to attachements of masticatory muscles. Dentists and general doctors should be aware that some of the maxillofacial manifestations of systematic scleroderma can be visible on panoramic radiographs.