Automatic interpretation of clock drawings for computerised assessment of dementia

The clock drawing test (CDT) is a standard neurological test for detection of cognitive impairment. A computerised version of the test has potential to improve test accessibility and accuracy. CDT sketch interpretation is one of the first stages in the analysis of the computerised test. It produces a set of recognised digits and symbols together with their positions on the clock face. Subsequently, these are used in the test scoring. This is a challenging problem because the average CDT taker has a high likelihood of cognitive impairment, and writing is one of the first functional activities to be affected. Current interpretation systems perform less well on this kind of data due to its unintelligibility. In this thesis, a novel automatic interpretation system for CDT sketch is proposed and developed. The proposed interpretation system and all the related algorithms developed in this thesis are evaluated using a CDT data set collected for this study. This data consist of two sets, the first set consisting of 65 drawings made by healthy people, and the second consisting of 100 drawings reproduced from drawings of dementia patients. This thesis has four main contributions. The first is a conceptual model of the proposed CDT sketch interpretation system based on integrating prior knowledge of the expected CDT sketch structure and human reasoning into the drawing interpretation system. The second is a novel CDT sketch segmentation algorithm based on supervised machine learning and a new set of temporal and spatial features automatically extracted from the CDT data. The evaluation of the proposed method shows that it outperforms the current state-of-the-art method for CDT drawing segmentation. The third contribution is a new v handwritten digit recognition algorithm based on a set of static and dynamic features extracted from handwritten data. The algorithm combines two classifiers, fuzzy k-nearest neighbour’s classifier with a Convolutional Neural Network (CNN), which take advantage both of static and dynamic data representation. The proposed digit recognition algorithm is shown to outperform each classifier individually in terms of recognition accuracy. The final contribution of this study is the probabilistic Situational Bayesian Network (SBN), which is a new hierarchical probabilistic model for addressing the problem of fusing diverse data sources, such as CDT sketches created by healthy volunteers and dementia patients, in a probabilistic Bayesian network. The evaluation of the proposed SBN-based CDT sketch interpretation system on CDT data shows highly promising results, with 100% recognition accuracy for heathy CDT drawings and 97.15% for dementia data. To conclude, the proposed automatic CDT sketch interpretation system shows high accuracy in terms of recognising different sketch objects and thus paves the way for further research in dementia and clinical computer-assisted diagnosis of dementia

Clock drawing test digit recognition using static and dynamic features

The clock drawing test (CDT) is a standard neurological test for detection of cognitive impairment. A computerised version of the test promises to improve the accessibility of the test in addition to obtaining more detailed data about the subject's performance. Automatic handwriting recognition is one of the first stages in the analysis of the computerised test, which produces a set of recognized digits and symbols together with their positions on the clock face. Subsequently, these are used in the test scoring. This is a challenging problem because the average CDT taker has a high likelihood of cognitive impairment, and writing is one of the first functional activities to be affected. Current handwritten digit recognition system perform less well on this kind of data due to its unintelligibility. In this paper, a new system for numeral handwriting recognition in the CDT is proposed. The system is based on two complementary sources of data, namely static and dynamic features extracted from handwritten data. The main novelty of this paper is the new handwriting digit recognition system, which combines two classifiers—fuzzy k-nearest neighbour for dynamic stroke-based features and convolutional neural network for static image- based features, which can take advantage of both static and dynamic data. The proposed digit recognition system is tested on two sets of data: first, Pendigits online handwriting digits; and second, digits from the actual CDTs. The latter data set came from 65 drawings made by healthy people and 100 drawings reproduced from the drawings by dementia patients. The test on both data sets shows that the proposed combination system can outperform each classifier individually in terms of recognition accuracy, especially when assessing the handwriting of people with dementi

Segmentation of clock drawings based on spatial and temporal features

The Clock Drawing Test (CDT) is an inexpensive and effective measure for early detection of cognitive impairment in the elderly, which is important for timely diagnosis and initiation of appropriate treatment. Currently, medical experts assess the drawings based on their judgement and a number of available scoring systems. An automatic system for assessment of CDT drawings would simultaneously decrease the waiting time for a specialist appointment and improve accessibility of the test to the patients. Published research has only started to address the problem of automatic assessment of CDT drawings and existing systems require user intervention during the segmentation of the CDT drawing into its composing parts, such as numbers and clock hands. In this paper, a new set of temporal and spatial features automatically extracted from the CDT data acquired using a graphics tablet is proposed. Consequently, a Support Vector Machine (SVM) classifier is employed to segment the CDT drawings into their elements, such as numbers and clock hands, on the basis of the extracted features. The proposed algorithm is tested on two data sets, the first set consisting of 65 drawings made by healthy people, and the second consisting of 100 drawings reproduced from actual drawings of dementia patients. The test on both data sets shows that the proposed method outperforms the current state-of-the-art method for CDT drawing segmentation

Flax Seed (Linum usitatissimum): the Healthy Medicinala and Preventive Food

اشتهرت بذور الكتان منذ القدم لدى الإغريق والفراعنة بكونها أحد أهم أنواع البذور الطبية ذات الفوائد العديدة للجسم، تعتبر بذور الكتان بشكل أساسي من المحاصيل التي اكتسبت اهتماماً كبيراً كونها غذاءً صحياً فقد أُثبت دورها الفعال في مجال أمراض القلب والأوعية الدموية وتقليل نسبة الدهون والسكر في الدم وكمواد مضادة لسرطان الثدي والقولون. كما ثبت أن بذور الكتان تمتلك خصائص  مضادة للأكسدة ومضادة للالتهابات في الدراسات التجريبية على الحيوانات وكذلك البشر. أفاد الباحثون أن المنتجات الغذائية التي تحتوي على بذور الكتان يمكن أن تحظى بقبول جيد للمستهلك إلى جانب فوائدها الغذائية. تسلط هذه المقالة الضوء على الدور الفعال لبذور الكتان في علاج العديد من الامراض او الوقاية منها باعتبارها كغذاء طبي ووقائي وعلاجي.Flaxseeds have been known since ancient times to the Greeks and Pharaohs for being one of the most important types of medicinal seeds with many benefits for the body. Flaxseed is mainly considered as one of the oil crops that has gained great attention as a health food, as it has proven its effective role in the field of cardiovascular diseases, reducing  the proportion of fat and sugar in the blood, and as an anti-cancer of the breast and colon.  Flaxseeds have also been shown to possess antioxidant and anti-inflammatory properties  in experimental studies in animals as well as humans. Researchers have reported that food products containing flaxseeds can have good consumer acceptance along with their nutritional benefits. This article highlights the effective role of flax seeds in the treatment or prevention of many diseases as a medicinal, preventive and curative food

Feature extraction method for clock drawing test

Recently, the number of elderly persons with dementia has been increasing. In the past, we proposed a dementia evaluation system using daily conversations and developed the system with a conversational robot. However, the current system is not ready for practical use because it can only evaluate time/geographical orientation and short-term memory, and some methods to evaluate other orientations and functions is required as well. In this paper, we discuss a new dementia evaluation system using not only daily conversations but also drawing tests. The authors employed a Clock Drawing Test (CDT) as a new dementia evaluation test and implemented it in a tablet device. This paper discusses a feature extraction and recognition method to distinguish normal cases from dementia cases. After evaluation experiments, the proposed method could recognize 87.6% of the clock drawing images

Clock drawing test interpretation system

A clock drawing test (CDT) is a neurological test used for the assessment of cognitive impairment based on sketches of a clock completed by a patient. Usually, a medical expert assesses the sketches to discover any deficiencies in the cognitive processes of the patient. More recently, automatic tools for assessing such tests have been developed. However, the problem of automatic interpretation of clock drawings, especially those sketched by people with cognitive impairment, is not fully solved, and in more difficult cases, the automatic systems have to revert to the help of human assessors in labelling the sketched objects forming the clock drawing. Moreover, the labelling of the sketched objects could be more reliable if prior knowledge of the expected CDT sketch structure and human reasoning could be integrated into the drawing interpretation system. This paper proposes a novel CDT sketch interpretation system, which represents the prior knowledge of the CDT structure by using ontology and integrating human reasoning through a fuzzy inference engine. The combination of the above technologies fuses multiple sources of information concerning the sketch structure and the visual appearance of the sketched objects whilst dealing with the interpretation uncertainty inherent to CDT sketches. The proposed CDT interpretation system is evaluated using two CDT data sets. The first data set consists of 65 drawings made by healthy people, while the second set contains 100 drawings reproduced from the drawings of dementia patients to simulate the kind of challenging sketches the system may have to work with. The evaluation analysis shows an improved interpretation performance of the proposed system in comparison with the classical approach, which does not receive benefits from the prior knowledge of the CDT sketch structure or simulated human reasoning

Physiological and histological Study on liver of male albino rats treated with retinol drug (retain)

Isotretinoin, commonly known as 13-cis-retinoic acid, is a vitamin derivative used to treat severe acne and several types of skin, head, and neck cancer. According to certain research, isotretinoin causes apoptosis in a number of different types of bodily cells. In instances of clinically obvious acute liver injury with jaundice, isotretinoin has not been definitively linked. It is unknown how isotretinoin elevates serum aminotransferase levels, however, given that this effect seems to occur more frequently with a greater dose of medication, it may be a direct harmful impact. Methods: This study was conducted in the animal house in the College of Science / University of Babylon for the period from 22/11/2021 to 22/12/2021. The study included 40 white male albino rats (Rattus rattus) aged 2-3 months, weighing 100 to 150 g, and at a temperature of (25+3C) and 12 u (light-dark cycle) and then divided into eight groups, each group containing five rats. The animals were left before treatment for two weeks to acclimatize. After that, the rats were treated with Isotretinoin and they were given free water and a ration