Gameplay as a source of intrinsic motivation in a randomized controlled trial of auditory training

Background: Previous studies of frequency discrimination training (FDT) for tinnitus used repetitive task-based training programmes relying on extrinsic factors to motivate participation. Studies reported limited improvement in tinnitus symptoms. Purpose: To evaluate FDT exploiting intrinsic motivations by integrating training with computer-gameplay. Methods: Sixty participants were randomly assigned to train on a conventional taskbased training, or one of two interactive game-based training platforms over six weeks. Outcomes included assessment of motivation, tinnitus handicap, and performance on tests of attention. Results: Participants reported greater intrinsic motivation to train on the interactive game-based platforms, yet compliance of all three groups was similar (~70%) and changes in self-reported tinnitus severity were not significant. There was no difference between groups in terms of change in tinnitus severity or performance on measures of attention. Conclusion: FDT can be integrated within an intrinsically motivating game. Whilst this may improve participant experience, in this instance it did not translate to additional compliance or therapeutic benefit

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

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