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176 results on '"Ahmed, Arfan"'

5. Artificial Intelligence for Predicting Responses to Thyroid Cancer Treatment

Author

Abd-Alrazaq, Alaa, AlSaad, Rawan, Ahmed, Arfan, Aslam, Hania, Salam, Babul, Aziz, Sarah, Sheikh, Javaid, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Xie, Xianghua, editor, Styles, Iain, editor, Powathil, Gibin, editor, and Ceccarelli, Marco, editor

Published
2024
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25. Morphological quantitation software in breast MRI : application to neoadjuvant chemotherapy patients

Author

Ahmed, Arfan, Gibbs, Peter, and Pickles, Martin Darren

Subjects
610, Medicine
Abstract

The work in this thesis examines the use of texture analysis techniques and shape descriptors to analyse MR images of the breast and their application as a potential quantitative tool for prognostic indication. Textural information is undoubtedly very heavily used in a radiologist’s decision making process. However, subtle variations in texture are often missed, thus by quantitatively analysing MR images the textural properties that would otherwise be impossible to discern by simply visually inspecting the image can be obtained. Texture analysis is commonly used in image classification of aerial and satellite photography, studies have also focussed on utilising texture in MRI especially in the brain. Recent research has focussed on other organs such as the breast wherein lesion morphology is known to be an important diagnostic and prognostic indicator. Recent work suggests benefits in assessing lesion texture in dynamic contrast-enhanced (DCE) images, especially with regards to changes during the initial enhancement and subsequent washout phases. The commonest form of analysis is the spatial grey-level dependence matrix method, but there is no direct evidence concerning the most appropriate pixel separation and number of grey levels to utilise in the required co-occurrence matrix calculations. The aim of this work is to systematically assess the efficacy of DCE-MRI based textural analysis in predicting response to chemotherapy in a cohort of breast cancer patients. In addition an attempt was made to use shape parameters in order to assess tumour surface irregularity, and as a predictor of response to chemotherapy. In further work this study aimed to texture map DCE MR images of breast patients utilising the co-occurrence method but on a pixel by pixel basis in order to determine threshold values for normal, benign and malignant tissue and ultimately creating functionality within the in house developed software to highlight hotspots outlining areas of interest (possible lesions). Benign and normal data was taken from MRI screening data and malignant data from patients referred with known malignancies. This work has highlighted that textural differences between groups (based on response, nodal status, triple negative and biopsy grade groupings) are apparent and appear to be most evident 1-3 minutes post-contrast administration. Whilst the large number of statistical tests undertaken necessitates a degree of caution in interpreting the results, the fact that significant differences for certain texture parameters and groupings are consistently observed is encouraging. With regards to shape analysis this thesis has highlighted that some differences between groups were seen in shape descriptors but that shape may be limited as a prognostic indicator. Using textural analysis gave a higher proportion of significant differences whilst shape analysis results showed inconsistency across time points. With regards to the mapping this work successfully analysed the texture maps for each case and established lesion detection is possible. The study successfully highlighted hotspots in the breast patients data post texture mapping, and has demonstrated the relationship between sensitivity and false positive rate via hotspot thresholding.

Published
2012

34. Chatbot features for anxiety and depression: A scoping review

Author

Ahmed, Arfan, primary, Hassan, Asmaa, additional, Aziz, Sarah, additional, Abd-alrazaq, Alaa A, additional, Ali, Nashva, additional, Alzubaidi, Mahmood, additional, Al-Thani, Dena, additional, Elhusein, Bushra, additional, Siddig, Mohamed Ali, additional, Ahmed, Maram, additional, and Househ, Mowafa, additional

Published
2023
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43. IMG-11. A COMPUTERISED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSING CHILDREN’S BRAIN TUMOURS USING FUNCTIONAL IMAGING AND MACHINE LEARNING

Author

Rose, Heather, primary, Ahmed, Arfan, additional, Babourina-Brooks, Ben, additional, Khan, Omar, additional, MacPherson, Lesley, additional, Manias, Karen, additional, Peake, Ashley, additional, Ali, Sana, additional, Withey, Stephanie, additional, Worthington, Lara, additional, Novak, Jan, additional, Zarinabad, Nilou, additional, Grundy, Richard, additional, Arvanitis, Theodoros, additional, and Peet, Andrew, additional

Published
2022
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44. Performance of Artificial Intelligence in Predicting Future Depression Levels.

Author

AZIZ, Sarah, ALSAAD, Rawan, ABD-ALRAZAQ, Alaa, AHMED, Arfan, and SHEIKH, Javaid

Abstract

Depression is a prevalent mental condition that is challenging to diagnose using conventional techniques. Using machine learning and deep learning models with motor activity data, wearable AI technology has shown promise in reliably and effectively identifying or predicting depression. In this work, we aim to examine the performance of simple linear and non-linear models in the prediction of depression levels. We compared eight linear and non-linear models (Ridge, ElasticNet, Lasso, Random Forest, Gradient boosting, Decision trees, Support vector machines, and Multilayer perceptron) for the task of predicting depression scores over a period using physiological features, motor activity data, and MADRAS scores. For the experimental evaluation, we used the Depresjon dataset which contains the motor activity data of depressed and non-depressed participants. According to our findings, simple linear and non-linear models may effectively estimate depression scores for depressed people without the need for complex models. This opens the door for the development of more effective and impartial techniques for identifying depression and treating/preventing it using commonly used, widely accessible wearable technology. [ABSTRACT FROM AUTHOR]

Published
2023
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45. Wearable AI Reveals the Impact of Intermittent Fasting on Stress Levels in School Children During Ramadan.

Author

AHMED, Arfan, AZIZ, Sarah, Abd-Alrazaq, Alaa, QIDWAI, Uvais, FAROOQ, Faisal, and SHEIKH, Javaid

Abstract

Intermittent fasting has been practiced for centuries across many cultures globally. Recently many studies have reported intermittent fasting for its lifestyle benefits, the major shift in eating habits and patterns is associated with several changes in hormones and circadian rhythms. Whether there are accompanying changes in stress levels is not widely reported especially in school children. The objective of this study is to examine the impact of intermittent fasting during Ramadan on stress levels in school children as measured using wearable artificial intelligence (AI). Twenty-nine school children (aged 13-17 years and 12M / 17F ratio) were given Fitbit devices and their stress, activity and sleep patterns analyzed 2 weeks before, 4 weeks during Ramadan fasting and 2 weeks after. This study revealed no statistically significant difference on stress scores during fasting, despite changes in stress levels being observed for 12 of the participants. Our study may imply intermittent fasting during Ramadan poses no direct risks in terms of stress, suggesting rather it may be linked to dietary habits, furthermore as stress score calculations are based on heart rate variability, this study implies fasting does not interfere the cardiac autonomic nervous system. [ABSTRACT FROM AUTHOR]

Published
2023
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46. Estimating Blood Glucose Levels Using Machine Learning Models with NonΙnvasive Wearable Device Data.

Author

AZIZ, Sarah, AHMED, Arfan, ABD-ALRAZAQ, Alaa, QIDWAI, Uvais, FAROOQ, Faisal, and SHEIKH, Javaid

Abstract

In 2019 alone, Diabetes Mellitus impacted 463 million individuals worldwide. Blood glucose levels (BGL) are often monitored via invasive techniques as part of routine protocols. Recently, AI-based approaches have shown the ability to predict BGL using data acquired by non-invasive Wearable Devices (WDs), therefore improving diabetes monitoring and treatment. It is crucial to study the relationships between non-invasive WD features and markers of glycemic health. Therefore, this study aimed to investigate accuracy of linear and non-linear models in estimating BGL. A dataset containing digital metrics as well as diabetic status collected using traditional means was used. Data consisted of 13 participants data collected from WDs, these participants were divided in two groups young, and Adult Our experimental design included Data Collection, Feature Engineering, ML model selection/development, and reporting evaluation of metrics. The study showed that linear and non-linear models both have high accuracy in estimating BGL using WD data (RMSE range: 0.181 to 0.271, MAE range: 0.093 to 0.142). We provide further evidence of the feasibility of using commercially available WDs for the purpose of BGL estimation amongst diabetics when using Machine learning approaches. [ABSTRACT FROM AUTHOR]

Published
2023
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