There is a general assumption that permanent teeth in children are emerging into the oral cavity earlier than the dates given in published scientific studies conducted many years ago. In the course of this research a rigorous experimental protocol was devised to provide reliable data collection and analysis methods and give contemporary emergence rate estimations with a strong scientific basis. In addition equations are presented to predict the chronological age of children using only the sex of the child and the number of permanent teeth present. Data was collected between April 1998 and July 2001 from 12,395 children between 4 and 15 years of age, in the Colchester area of the UK. The results show that the ages of emergence of the permanent teeth are later than previously assumed. This research also confirms previous research showing that girl's teeth emerge before boy's teeth, that there is no statistical difference in the age of emergence contra-lateral teeth in the same arch and that there is a statistical difference in the age of emergence of ipsi-lateral teeth in opposing arches.

京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第13162号 / 論医博第2149号 / 新制||医||1029(附属図書館) / (主査)教授 武田 俊一, 教授 松田 文彦, 教授 清水 章 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

UK Biobank is a British clinical study containing over 40 000 Magnetic Resonance Images (MRI) with 100 000 MRI planned of participants aged 44-82 as well as a large amount of related medical data. Analyzing these images with a neural network to find relations between the information in an MRI image and various medical data could lead to interesting medical revelations. While other studies usually focus on improving the network architecture, we instead propose a method to get targeted information out of full body MRI images. This is done by sampling various sub-volumes of the full body images and making a collage specifically tailored to the problem at hand before feeding them to a ResNet50 based network. The images are further analyzed using saliency analysis in order to gain information on what regions the network found important. This method was attempted on a variety of medical data including age, kidney volume, liver fat percentage, and heart volume. The method is used both as a way to increase information density in the input images as well as restricting information, such that we can see how well the network can predict about some medical data point from only some part of the body.The collages are able to increase the information in the images while the more complex representation and non-continuous representation does not cause problems for the network. These collages are also conducive to getting clearer and sharper saliency maps, which may give interesting medical information by showing what regions the network considers relevant. This may reveal otherwise difficult to notice relations between the information in the MRI images and medical information.

Electrocardiography (ECG) is a non-invasive method used in medicine to track the electrical pulses sent by the heart. The time between two subsequent electrical impulses and hence the heartbeat of a subject, is referred to as an RR interval. Previous studies show that RR intervals can be used for identifying sleep patterns and cardiovascular diseases. Additional research indicates that RR intervals can be used to predict the cardiovascular age of a subject. This thesis investigates, if this assumption is true, based on two different datasets as well as simulated data based on Gaussian Processes. The datasets used are Holter recordings provided by the University of Gdańsk as well as a dataset provided by Physionet. The former represents a balanced dataset of recordings during nocturnal sleep of healthy subjects whereas the latter one describes an imbalanced dataset of records of a whole day of subjects that suffered from myocardial infarction. Feature-based models as well as a deep learning architecture called DeepSleep, based on a paper for sleep stage detection, are trained. The results show, that the prediction of a subject's age, only based in RR intervals, is difficult. For the first dataset, the highest obtained test accuracy is 37.84 per cent, with a baseline of 18.23 per cent. For the second dataset, the highest obtained accuracy is 42.58 per cent with a baseline of 39.14 per cent. Furthermore, data is simulated by fitting Gaussian Processes to the first dataset and following a Bayesian approach by assuming a distribution for all hyperparameters of the kernel function in use. The distributions for the hyperparameters are continuously updated by fitting a Gaussian Process to a slices of around 2.5 minutes. Then, samples from the fitted Gaussian Process are taken as simulated data, handling impurity and padding. The results show that the highest accuracy achieved is 31.12 per cent with a baseline of 18.23 per cent. Concludingly, cardiovascular age prediction based on RR intervals is a difficult problem and complex handling of impurity does not necessarily improve the results.

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Breast cancer is the most common cancer type for women worldwide. Early detection is key to improve prognosis and treatment success. A cost-efficient way of finding breast cancer early is mammography screening on a population basis. A major issue with mammography screening is in-between screening cancers. One method of targeting this issue is calculating breast cancer risk stratification on healthy mammography scans, however, this method is as of today insufficient. One proposed addition to refine risk stratification is to use Artificial Intelligence guided age prediction. The aim of this study was to investigate to what extent there is an additive value of age prediction on breast cancer risk stratification. Convolutional Neural Networks (CNNs) were used to train a model on an age prediction task using healthy mammography scans from the Cohort of Screen-Aged Women. The predicted ages and delta ages, calculated as predicted age minus chronological age, were then added to a logistic regression task together with, and without, the known risk factor mammographic density. The results showed an increase in breast cancer detection with the risk model incorporating age prediction for some age groups. This suggests age prediction using CNNs might increase breast cancer detection. More studies are needed to confirm these findings. / Bröstcancer är den vanligaste cancertypen för kvinnor globalt. Tidig upptäckt är en nyckelfaktor för att förbättra prognos och behandlingsframgång. Ett kostnadseffektivt sätt att hitta tidigt utvecklad bröstcancer är allmän screening med mammografi. Ett problem med denna screening är cancer som uppkommer mellan screeningtillfällen. En metod för att lösa detta problem är riskstratifiering som ämnar att beräkna risken att utveckla cancer från friska mammografibilder, men denna metod är idag otillräcklig. Ett förslag på tillägg för att förfina resultatet av detta är att använda artificiell intelligens guidad åldersbedömning. I den här studien var syftet att undersöka i vilken utsträckning det finns ett additivt värde av åldersbedömning för modellering av risken att utveckla bröstcancer. Convolutional Neural Networks (CNNs) användes för att träna en åldersbedömningssmodell på friska mammografibilder från Cohort of ScreenAged Women. De bedömda åldrarna samt deltaåldrarna, beräknade som bedömd ålder minus kronologisk ålder, användes sedan som input till en logistisk regressionsuppgift tillsammans med, samt utan, den kända riskfaktorn mammografisk densitet. Resultaten visade en ökad upptäckt av bröstcancer för vissa åldersgrupper då riskmodellen inkluderade deltaåldrarna. Detta tyder på att åldersbedömning med CNNs kan öka upptäckten av bröstcancer. Fler studier behövs för att bekräfta dessa fynd.