7 research outputs found
Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm
Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.ope
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Video Bioinformatics: Human Embryonic Stem Cell Analysis With Machine Learning
Human Embryonic Stem Cell (hESC) have a great potential for regenerative medicine to provide treatments for Parkinsonās disease, Huntingtonās disease, Type 1 diabetes mellitus, etc. Consequently, hESC are often used as a model in the biological assay to study the effects of chemical agents on the human body. Video analysis plays an important role for biological assays in the field of prenatal toxicology and stem cell differentiation. This thesis introduces machine learning techniques for detection, segmentation and classification for hESC analysis. For the detection, a bio-driven algorithm was used to detect cell regions in hESC images. Cell region detection is essential in stem cell focused analysis. It can prevent background information from contaminating the analysis and put more emphasis on processing the cell region. For the segmentation part, a bio-inspired method was proposed for bleb extraction and analysis over time. Bleb formation is a strong health indicator of the stem cell undergoing chemical reactions. Therefore, it is significant to biologist to analyze the formation process over time. For the classification, a deep learning structure was built with both labeled and unlabeled hESC data to classify the six common classes in stem cell images. The six classes are: 1). cell clusters, 2). debris, 3). unattached cells, 4). attached cells, 5). dynamically blebbing cells, and 6). apoptotically blebbing cells. Various results are provided on real video datasets collected using a phase contrast microscope and a Nikon Bio-station