PhD Studentship: GAIA: Generating Artificial Intelligence Assisted Assessment for Alopecia
4 weeks ago
Project summary
The impact of alopecia is highlighted in the Global Burden of Disease estimates of years lost to disability, where alopecia areata (AA) was ranked higher than both psoriasis and melanoma in disease impact.
Irrespective of the underlying cause or pattern of the hair loss (diffuse thinning or patches), accurate measures of a person’s hair loss are difficult. Before significant diffuse hair thinning becomes clinically appreciable, over 50% scalp hairs need to be lost. Therefore, the process may have been occurring for many months or years before someone presents to their doctor. Current treatments are reasonable at stabilising hair loss but not very good at reversing it. Therefore, being able to identify the problem and treat early in the course of the disease would result in better outcomes for the patient.
We are looking for candidates to research computer vision and artificial intelligence techniques to create an objective method of measuring areas of hair loss/recovery. This will tackle the problems of current technology, where it can be time-consuming, expensive, or require shaving of a patient’s hair.
The successful candidate will benefit from training facilities in The Manchester Metropolitan University and The Dermatology Centre, University of Manchester, Salford Royal NHS Foundation Trust.
Aims and objectives
The aim of this project is to predict the rate of hair loss or recovery in people with alopecia using computer vision and Artificial Intelligence (AI) algorithms.
To achieve this aim, the main objectives are:
- Automate the hair segmentation process and predict the hair line.
- Design a classification algorithm to quantify the hair loss severity.
- Develop mobile phone app as a point of care for people with alopecia.
- Evaluate novel AI assessments using multimodal imaging.
- Validate performance of the AI technology in clinical settings.
Specific requirements of the project
Candidates must have a strong motivation for research and excellent programming skills. Expertise of developing computer vision and machine learning algorithms would be desirable, with an interest in image analysis.
Qualifications
- A high-grade undergraduate degree (first class or upper second) in Computer Science or MSc in related field
Skills
- Knowledge of software development and programming
- Good communication and writing skills
- Developing image analysis/machine learning algorithms would be beneficial
- Able to work as part of a joint academia and clinical team
How to apply
Interested applicants should contact Dr Adrian Davison for an informal discussion.
To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technology (or download the PGR application form).
You should also complete the PGR thesis proposal form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest.
If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk. Closing date 8 June 2024. Expected start date October 2024.
Please quote the reference: SciEng-AD-2024-GAIA
Home fees are covered. Eligible international students will need to make up the difference in tuition fee funding where funding is available
£19,237 for 2024/25
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Manchester, United Kingdom Manchester Metropolitan University Full timeProject summaryThe impact of alopecia is highlighted in the Global Burden of Disease estimates of years lost to disability, where alopecia areata (AA) was ranked higher than both psoriasis and melanoma in disease impact.Irrespective of the underlying cause or pattern of the hair loss (diffuse thinning or patches), accurate measures of a person's hair loss...
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Manchester, United Kingdom Manchester Metropolitan University Full timeProject summaryThe impact of alopecia is highlighted in the Global Burden of Disease estimates of years lost to disability, where alopecia areata (AA) was ranked higher than both psoriasis and melanoma in disease impact.Irrespective of the underlying cause or pattern of the hair loss (diffuse thinning or patches), accurate measures of a person's hair loss...
-
Manchester, United Kingdom Manchester Metropolitan University Full timeProject summary The impact of alopecia is highlighted in the Global Burden of Disease estimates of years lost to disability, where alopecia areata (AA) was ranked higher than both psoriasis and melanoma in disease impact. Irrespective of the underlying cause or pattern of the hair loss (diffuse thinning or patches), accurate measures of a person’s hair...
-
Manchester, United Kingdom Manchester Metropolitan University Full timeProject summaryThe impact of alopecia is highlighted in the Global Burden of Disease estimates of years lost to disability, where alopecia areata (AA) was ranked higher than both psoriasis and melanoma in disease impact.Irrespective of the underlying cause or pattern of the hair loss (diffuse thinning or patches), accurate measures of a person's hair loss...
-
Manchester, United Kingdom Manchester Metropolitan University Full timeProject summaryThe impact of alopecia is highlighted in the Global Burden of Disease estimates of years lost to disability, where alopecia areata (AA) was ranked higher than both psoriasis and melanoma in disease impact.Irrespective of the underlying cause or pattern of the hair loss (diffuse thinning or patches), accurate measures of a person's hair loss...
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