Regeneration of the Periodontal Apparatus in Aggressive Periodontitis Patients

The purpose of this study is to evaluate and compare, retrospectively, the outcome of two different periodontal regeneration procedures in patients suffering from aggressive periodontitis (AgP). Twenty-eight patients were diagnosed with AgP, suffering from several intra-bony defects (IBD); that were treated by one of two periodontal regeneration techniques randomly assigned to each patient: a. guided tissue regeneration (GTR) or b. an application of extracted enamel matrix derivatives (EMD) combined with demineralized bone xenograft particles (DBX). Probing pocket depth (PPD), clinical attachment level (CAL), and gingival recession were recorded. Pre-treatment and follow-up (up to 10 years from the surgery) recordings were analyzed statistically within and between groups. A significant reduction was shown at time on PPD and CAL values, however, not between subject groups. CAL values decreased in all sites. At the EMD group (44 sites), CAL gain was 1.92 mm (±1.68) from pre-treatment to follow-up (p < 0.001) and at the GTR group (12 sites) CAL gain of 2.27 (±1.82) mm. In conclusion, 1–10 years observations have shown that surgical treatment of AgP patients by either GTR or by application of EMD/DBX results in similar successful clinical results

Deep convolution neural network for screening carotid calcification in dental panoramic radiographs

Ischemic stroke, a leading global cause of death and disability, is commonly caused by carotid arteries atherosclerosis. Carotid artery calcification (CAC) is a well-known marker of atherosclerosis. Such calcifications are classically detected by ultrasound screening. In recent years it was shown that these calcifications can also be inferred from routine panoramic dental radiographs. In this work, we focused on panoramic dental radiographs taken from 500 patients, manually labelling each of the patients’ sides (each radiograph was treated as two sides), which were used to develop an artificial intelligence (AI)-based algorithm to automatically detect carotid calcifications. The algorithm uses deep learning convolutional neural networks (CNN), with transfer learning (TL) approach that achieved true labels for each corner, and reached a sensitivity (recall) of 0.82 and a specificity of 0.97 for individual arteries, and a recall of 0.87 and specificity of 0.97 for individual patients. Applying and integrating the algorithm in healthcare units and dental clinics has the potential of reducing stroke events and their mortality and morbidity consequences. Author summary Stroke is a leading global cause of death and disability. One major cause of stroke is carotid arteries atherosclerosis. Carotid artery calcification (CAC) is a well-known marker of atherosclerosis. Traditional approaches for CAC detection are doppler ultrasound screening and angiography computerized tomography (CT), medical procedures that incur financial expenses, and are time consuming and discomforting to the patient. Of note, angiography CT involves the injection of contrast material and exposure to X-ray ionizing irradiation. In recent years researchers have shown that CAC can also be detected by analyzing routine panoramic dental radiographs, a non-invasive, cheap and easily accessible procedure. This study takes us one step further, in developing artificial intelligence (AI)-based algorithms trained to detect such calcifications in panoramic dental radiographs. The models developed are based on deep learning convolutional neural networks and transfer learning, that enable an accurate automated detection of carotid calcifications, with a recall of 0.82 and a specificity of 0.97. Statistical approaches for assessing predictions per individual (i.e.: predicting the risk of calcification in at least one artery) were developed, showing a recall of 0.87 and specificity of 0.97. Applying and integrating this approach in healthcare units may significantly contribute to identifying at-risk patients

The probabilities and quantities of prediction CAC or “Clean” on three types of patients based on the performance of a single side.

The cells in the table consist of the probabilities and the patient numbers are presented in italic in parentheses.</p

The region of interest is located near the C3-C4 spinals.

Therefore, we cut out each of the lower corners of the panoramic image in 500 X 500 pixels size (depicted with light blue rectangles). This size ensured that the corners encompassed the calcification.</p

Sample size sufficiency analysis.

Ischemic stroke, a leading global cause of death and disability, is commonly caused by carotid arteries atherosclerosis. Carotid artery calcification (CAC) is a well-known marker of atherosclerosis. Such calcifications are classically detected by ultrasound screening. In recent years it was shown that these calcifications can also be inferred from routine panoramic dental radiographs. In this work, we focused on panoramic dental radiographs taken from 500 patients, manually labelling each of the patients’ sides (each radiograph was treated as two sides), which were used to develop an artificial intelligence (AI)-based algorithm to automatically detect carotid calcifications. The algorithm uses deep learning convolutional neural networks (CNN), with transfer learning (TL) approach that achieved true labels for each corner, and reached a sensitivity (recall) of 0.82 and a specificity of 0.97 for individual arteries, and a recall of 0.87 and specificity of 0.97 for individual patients. Applying and integrating the algorithm in healthcare units and dental clinics has the potential of reducing stroke events and their mortality and morbidity consequences.</div

MapGrad of calcified corners.

Ischemic stroke, a leading global cause of death and disability, is commonly caused by carotid arteries atherosclerosis. Carotid artery calcification (CAC) is a well-known marker of atherosclerosis. Such calcifications are classically detected by ultrasound screening. In recent years it was shown that these calcifications can also be inferred from routine panoramic dental radiographs. In this work, we focused on panoramic dental radiographs taken from 500 patients, manually labelling each of the patients’ sides (each radiograph was treated as two sides), which were used to develop an artificial intelligence (AI)-based algorithm to automatically detect carotid calcifications. The algorithm uses deep learning convolutional neural networks (CNN), with transfer learning (TL) approach that achieved true labels for each corner, and reached a sensitivity (recall) of 0.82 and a specificity of 0.97 for individual arteries, and a recall of 0.87 and specificity of 0.97 for individual patients. Applying and integrating the algorithm in healthcare units and dental clinics has the potential of reducing stroke events and their mortality and morbidity consequences.</div

Summary—performances ‘per corner ‘and ‘per patient’.

Summary—performances ‘per corner ‘and ‘per patient’.</p

Recall-Precision (RP) curves of all the cross-validation folds (each fold is shown here in a different curve); the random classifier is depicted with a dashed line.

Recall-Precision (RP) curves of all the cross-validation folds (each fold is shown here in a different curve); the random classifier is depicted with a dashed line.</p

Gradient-weighted class activation mapping (Grad-CAM) of a correct prediction of CAC and “clean” classes.

The colors indicate the region that has the greatest impact on the CAC prediction. The color range is presented below (ranging from red, representing the regions with the greatest impact on the CAC prediction, to blue, representing the regions with the lowest impact). It can be noticed that the classifier indeed focused on the region of the calcification signs. Other Grad-CAM of CAC and clean predications can be found in the S3 Fig and S4 Fig.</p

Sample size assessment: F1 score as a function of the sample size, based on random subsampling of 40%-80% of the original data.

Sample size assessment: F1 score as a function of the sample size, based on random subsampling of 40%-80% of the original data.</p