Merge pull request #25 from nhsengland/sh_skunkworks

Sh skunkworks
nhsengland · Dec 22, 2023 · 87e672d · 87e672d
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diff --git a/docs/our_work/adrenal-lesions.md b/docs/our_work/adrenal-lesions.md
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+---
+title: 'Using deep learning to detect adrenal lesions in CT scans'
+summary: 'This project explored whether applying AI and deep learning augment the detection of adrenal incidentalomas in patients’ CT scans.'
+category: 'Projects'
+origin: 'Skunkworks'
+tags: ['classification','lesion detection','vision AI']
+---
+
+Many cases of adrenal lesions, known as adrenal incidentalomas, are discovered incidentally on CT scans performed for other medical conditions. These lesions can be malignant, and so early detection is crucial for patients to receive the correct treatment and allow the public health system to target resources efficiently. Traditionally, the detection of adrenal lesions on CT scans relies on manual analysis by radiologists, which can be time-consuming and unsystematic.
+
+The main aim of this study was to examine whether or not using AI can improve the detection of adrenal incidentalomas in CT scans. Previous studies have suggested that AI has the potential in distinguishing different types of adrenal lesions. In this study, we specifically focused on detecting the presence of any type of adrenal lesion in CT scans. To demonstrate this proof-of-concept, we investigated the potential of applying deep learning techniques to predict the likelihood of a CT abdominal scan presenting as ‘normal’ or ‘abnormal’, the latter implying the presence of an adrenal lesion.
+## Results
+
+
+Output|Link
+---|---
+Open Source Code & Documentation|[Github](https://github.com/nhsx/skunkworks-adrenal-lesions-detection)
+Case Study|[Case Study](https://transform.england.nhs.uk/ai-lab/explore-all-resources/develop-ai/using-deep-learning-to-detect-adrenal-lesions-in-ct-scans/)
+Technical report|[medRxiv](https://www.medrxiv.org/content/10.1101/2023.02.22.23286184v1)
+
+[comment]: <> (The below header stops the title from being rendered (as mkdocs adds it to the page from the "title" attribute) - this way we can add it in the main.html, along with the summary.)
+#
diff --git a/docs/our_work/ai-deep-dive.md b/docs/our_work/ai-deep-dive.md
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+---
+title: 'AI Deep Dive'
+summary: 'The NHS AI Lab Skunkworks team have developed and delivered a series of workshops to improve confidence working with AI.'
+category: 'Playbooks'
+origin: 'Skunkworks'
+tags: []
+---
+
+### Motivation
+
+A series of practical workshops designed to increase confidence, trust and capability of implementing AI within the NHS and Social Care sector, based on the experience of the AI Lab Skunkworks team.
+
+### Audience
+
+Clinicians, technology teams, operations teams, and other stakeholders from organisations interested in utilising AI
+
+### Pre-requisites
+
+* I understand there is great potential for AI in Health and Care
+* I want to increase my understanding about the practical application of AI in Health and Care
+* I understand the variety and quantity of data in my organisation
+* I'm willing to embrace being experimental and open to learning from experience 
+
+### Attendees
+
+10 or 12 attendees max 
+
+### Your presenters
+
+Workshops run by NHS AI Lab Skunkworks team for one organisation (e.g. Trust) at a time.
+
+### Format
+
+A series of weekly 75 minute workshops, delivered online through Google Meet or Microsoft Teams
+
+### By the end of the workshop series, learners will be able to
+
+* Be confident in having more conversations about AI in Health and Care 
+* Embrace an experimental approach to AI in Health and Care
+* Understand practical steps required for experimenting with AI in Health and Care
+* Create a detailed plan for an AI project
+
+## Workshop 1: AI fundamentals 
+
+### Aim
+
+Establish baseline understanding of AI and what is possible
+
+### Key topics
+
+* Define AI, Machine Learning and Data Science
+* Understand the two AI families (Narrow and General)
+* What's possible with ML
+* Ethics considerations
+* The AI Life Cycle 
+* Examples of AI in Health and Care
+* Examples of projects we’ve worked on
+
+### By the end of this workshop, learners will
+
+* Have a baseline understanding of AI & Machine Learning
+* Be familiar with AI case studies in health and care
+* Be excited about the potential for AI in their organisation
+
+## Workshop 2: Problem Discovery
+
+### Aim
+
+Develop skills to identify and communicate problems
+
+### Key topics: 
+
+* Problem identification
+* Identifying stakeholders
+* Understanding user needs 
+* Writing a user story 
+* Capturing the user journey
+
+### By the end of this workshop, learners will
+
+* Have clearly defined problems they are facing
+* Have identified stakeholder and user needs
+* Documented the user journey
+
+## Workshop 3: Solution Discovery
+
+### Aim
+
+Identify solutions and potential AI technologies for a problem
+
+### Key topics
+
+* Solution identification
+* Appropriate AI technologies
+* Intended outcomes: Press Release
+
+### By the end of this workshop, learners will
+
+* Generate potential solutions for their problem
+* Evaluate AI technologies as part of the solution
+* Draft a “Press Release” for the future state
+
+## Workshop 4: Practicalities
+
+### Aim
+
+To understand the practical aspects of every AI project.
+
+### Key topics
+
+* Data Data Data: how much, where from
+* Information Governance (IG)
+* Regulatory frameworks
+* Ethics approvals
+
+### By the end of this workshop, learners will
+
+* Identify the data needs of an AI project
+* Understand how to work with Information Governance
+* Understand the regulatory requirements for a project
+* Understand ethical frameworks applicable to AI projects
+
+## Workshop 5: Launching your AI experiment
+
+### Aim
+
+To understand the next steps in launching your AI Experiment 
+
+### Key Topics
+
+* Business and technical due diligence
+* Build vs Buy?
+* Team make up and roles
+* Partnering with Skunkworks, AI Award, AHSN
+* Keeping up to date with developments in AI
+
+### By the end of this workshop, learners will
+
+* Understand the need for business and technical due diligence
+* Understand the balance of build vs buy
+* Have a robust understanding of what they need to launch their AI experiment
+* Be connected to the wider AI community within the NHS and care sector
+
+## Book your sessions
+
+If you'd like to arrange an AI Deep Dive with your team, please [get in touch](mailto:[email protected]?subject=AI%20Deep%20Dive%20enquiry).
+
+[comment]: <> (The below header stops the title from being rendered (as mkdocs adds it to the page from the "title" attribute) - this way we can add it in the main.html, along with the summary.)
+#
diff --git a/docs/our_work/ai-dictionary.md b/docs/our_work/ai-dictionary.md
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+---
+title: 'AI Dictionary'
+summary: 'A simple dictionary of common AI terms with a health and care context.'
+category: 'Projects'
+origin: 'Skunkworks'
+tags : ['ai', 'dictionary']
+---
+
+[![AI Dictionary](../images/ai-dictionary.png)](https://nhsx.github.io/ai-dictionary)
+
+AI is full of acronyms and a common understanding of technical terms is often lacking.
+
+We decided to create a simple, open source, AI dictionary of terms with a health and care context to help level up those working in the field.
+
+## Results
+
+A front-end website: [https://nhsx.github.io/ai-dictionary](https://nhsx.github.io/ai-dictionary) written in HTML/CSS/JavaScript (frontend) with a JSON schema driven database of terms.
+
+Output|Link
+---|---
+Open Source Code & Documentation|[Github](https://github.com/nhsx/ai-dictionary)
+Case Study|N/A
+Technical report|N/A
+Algorithmic Impact Assessment|N/A
+
+[comment]: <> (The below header stops the title from being rendered (as mkdocs adds it to the page from the "title" attribute) - this way we can add it in the main.html, along with the summary.)
+#
diff --git a/docs/our_work/ambulance-delay-predictor.md b/docs/our_work/ambulance-delay-predictor.md
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+---
+title: 'Ambulance Handover Delay Predictor'
+summary: 'Predict ambulance delays at hospital, with reasons, to allow them to influence hospitals'' behaviour to mitigate against queues before they happen.'
+category: 'Projects'
+origin: 'Skunkworks'
+tags: ['ambulance','handover delay','predictor','random forest', 'decision tree', 'classification', 'time series']
+---
+
+![Ambulance Handover Delay Predictor screenshot](../images/ambulance-delay-predictor.png)
+
+Ambulance Handover Delay Predictor was selected as a project in Q2 2022 following a succesful pitch to the AI Skunkworks problem-sourcing programme.
+
+## Results
+
+A proof-of-concept demonstrator written in Python (machine learning model, Jupyter Notebooks).
+
+Output|Link
+---|---
+Open Source Code & Documentation|[Github](https://github.com/nhsx/skunkworks-ambulance-queueing-prediction)
+Technical report|[PDF](https://github.com/nhsx/skunkworks-ambulance-queueing-prediction/raw/main/docs/ambulance-queueing-prediction-technical-report.pdf)
+
+[comment]: <> (The below header stops the title from being rendered (as mkdocs adds it to the page from the "title" attribute) - this way we can add it in the main.html, along with the summary.)
+#
diff --git a/docs/our_work/bed-allocation.md b/docs/our_work/bed-allocation.md
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+---
+title: 'Bed allocation'
+summary: 'Machine learning to effectively aid bed management in Kettering General Hospital.'
+category: 'Projects'
+origin: 'Skunkworks'
+tags: ['bed management','bayesian forecasting','monte carlo tree search','greedy allocation']
+---
+
+![Bed allocation screenshot](../images/bed-allocation.png)
+
+Bed allocation was identified as a suitable opportunity for the AI Skunkworks programme in May 2021.
+
+## Results
+
+A proof-of-concept demonstrator written in Python (backend, virtual hospital, models) and HTML/CSS/JavaScript (frontend).
+
+Output|Link
+---|---
+Open Source Code & Documentation|[Github](https://github.com/nhsx/skunkworks-bed-allocation)
+Case Study|[Case Study](https://www.nhsx.nhs.uk/ai-lab/explore-all-resources/develop-ai/improving-hospital-bed-allocation-using-ai/)
+Technical report|[PDF](https://github.com/nhsx/skunkworks-bed-allocation/blob/main/docs/NHS_AI_Lab_Skunkworks_Bed_Allocation_Technical_Report.pdf)
+
+[comment]: <> (The below header stops the title from being rendered (as mkdocs adds it to the page from the "title" attribute) - this way we can add it in the main.html, along with the summary.)
+#
diff --git a/docs/our_work/casestudy-adrenal.md b/docs/our_work/casestudy-adrenal.md
@@ -0,0 +1,73 @@
+---
+title: 'Using deep learning to detect adrenal lesions in CT scans'
+summary: 'Augmenting the detection of adrenal incidentalomas in patients’ CT scans.'
+category: 'CaseStudies'
+origin: 'Skunkworks'
+tags: ['vision AI','classification','deep learning', 'pathology', 'neural networks']
+---
+
+## Info
+This is a backup of the case study published [here](https://transform.england.nhs.uk/ai-lab/explore-all-resources/develop-ai/using-deep-learning-to-detect-adrenal-lesions-in-ct-scans/) on the NHS England Transformation Directorate website.
+
+## Case Study
+Many cases of adrenal lesions, known as adrenal incidentalomas, are discovered incidentally on CT scans performed for other medical conditions. These lesions can be malignant, and so early detection is crucial for patients to receive the correct treatment and allow the public health system to target resources efficiently. Traditionally, the detection of adrenal lesions on CT scans relies on manual analysis by radiologists, which can be time-consuming and unsystematic.
+
+
+**The challenge**  
+Can applying AI and deep learning augment the detection of adrenal incidentalomas in patients’ CT scans?
+
+
+### Overview
+Autopsy studies reveal a statistic that as many as 6% of all natural deaths displayed a previously undiagnosed adrenal lesion. Such lesions are also found incidentally (and are therefore referred to as adrenal incidentalomas) in approximately 1% of chest or abdominal CT scans. These lesions affect approximately 50,000 patients annually in the United Kingdom, with significant impact on patient health, including 10% to 15% of cases of excess hormone production, or 1% to 5% of cases of cancer.
+
+It is a significant challenge for the health care system to, in a standardised way, promptly reassure the majority of patients, who have no abnormalities, whilst effectively focusing on those with hormone excess or cancers. Issues include over-reporting (false positives), causing patient anxiety and unnecessary investigations (wasting resources of the health care system), and under-reporting (missed cases), with potentially fatal outcomes. This has major impacts on patient well-being and clinical outcomes, as well as cost-effectiveness.
+
+The main aim of this study was to examine whether or not using Artificial Intelligence (AI) can improve the detection of adrenal incidentalomas in CT scans. Previous studies have suggested that AI has the potential in distinguishing different types of adrenal lesions. In this study, we specifically focused on detecting the presence of any type of adrenal lesion in CT scans. To demonstrate this proof-of-concept, we investigated the potential of applying deep learning techniques to predict the likelihood of a CT abdominal scan presenting as ‘normal’ or ‘abnormal’, the latter implying the presence of an adrenal lesion.
+
+### What we did
+Using the data provided by University Hospitals of North Midlands NHS Trust, we developed a 2.5D deep learning model to perform detection of adrenal lesions in patients’ CT scans (binary classification of normal and abnormal adrenal glands). The entire dataset is completely anonymised and does not contain any personal or identifiable information of patients. The only clinical information taken were the binary labels for adrenal lesions (‘normal’ or ‘abnormal’) for the pseudo-labelled patients and their CT scans.
+
+#### 2.5D images
+A 2.5D image is a type of image that lies between a typical 2D and 3D image. It can retain some level of 3D features and can potentially be processed as a 2D image by deep learning models. A greyscale 2D image is two dimensional with a size of x × y, where x and y are the length and width of the 2D image. For a greyscale 3D image (e.g., a CT scan), with a size of x × y × n, it can be considered as a combination of a stack of n number of greyscale 2D images. In other words, a CT scan is a 3D image consisting of multiple 2D images layered on top of each other. The size of a 2.5D image is x × y × 3, and it represents a stack of 3 greyscale 2D images.
+
+Typically, an extra dimension of pixel information is required to record and display 2D colour images in electronic systems, such as the three RGB (red, green, and blue) colour channels. This increases the size of a 2D image to x × y × 3, where the 3 represents the three RGB channels. Many commonly used families of 2D deep learning algorithms (e.g., VGG, ResNet, and EfficientNet) have taken colour images into account and have the ability to process images with the extra three channels. Taking the advantage of the fact that pixel volumes have the same size between 2D colour images and 2.5D images, converting our 3 dimensional CT scan data to 2.5D images can allow us to apply 2D deep learning models on our images.
+
+#### Why using a 2.5D model
+Due to the intrinsic nature of CT scans (e.g., a high operating cost, limited number of available CT scanners, and patients’ exposure to radiation), the acquisition of a sufficient amount of CT scans for 3D deep learning models training is challenging. In many cases, the performance of 3D deep learning models is limited by the small and non-diversified dataset. Training, validating, and testing the model with a small dataset can lead to many disadvantages, for example, a high risk of overfitting the training-validation set (low prediction ability on an unseen test set), and evaluating the model performance within the ambit of a small number statistic (underrepresented test set results in the test accuracy much lower/higher than the underlying model performance).
+
+To overcome some of the disadvantage of training a 3D deep learning model, we took a 2.5D deep learning model approach in this case study. Training the model using 2.5D images enables our deep learning model to still learn from the 3D features of the CT scans, while increasing the number of training and testing data points in this study. Moreover, we can apply 2D deep learning models to the set of 2.5D images, which allow us to apply transfer learning to train our own model further based on the knowledge learned by other deep learning applications (e.g., ImageNet, and the NHS AI Lab’s National COVID-19 Chest Imaging Database).
+
+![Adrenal flow of transfer](../images/Flow_of_transfer.width-800.png)
+
+#### Classification of 3D CT scans
+To perform the binary classification on the overal CT scans (instead of a single 2.5D image), the classification results from each individual 2.5D image that make up a CT scan are considered.
+
+To connect the classification prediction results from the 2.5D images to the CT scan, we introduce an operating value for our model to provide the final classification. The CT scans are classified as normal if the number of abnormal 2.5D images is lower than the threshold operating value. For example, if the operating value is defined to be X, a CT scan will be considered as normal if there are more than X of its 2.5D images classified as normal by our model.
+
+#### Processing the CT scans to focus on the adrenal glands
+
+To prepare the CT scans for this case study (region of interest focus on the adrenal grands), we also developed a manual 3D cropping tool for CT scans. This cropping applied to all three dimensions, including a 1D cropping to select the appropriate axial slices and a 2D cropping on each axial slice. The final cropped 3D image covered the whole adrenal gland on both sides with some extra margin on each side.
+
+![Adrenal cropping](../images/Cropping_process.width-800.png)
+
+### Outcomes and lessons learned
+
+[The resulting code, released as open source on our](https://github.com/nhsx/skunkworks-adrenal-lesions-detection) Github (available to anyone to re-use), enables users to:
+
+-   Process CT scans to focus on the region of interest (e.g., adrenal glands),
+-   Transform 3D CT scans to sets of 2.5D images,
+-   Train a deep learning model with the 2.5D images for adrenal lesion detection (classification: normal vs. abnormal),
+-   Evaluate the trained deep learning model on an independent test set.
+
+This proof-of-concept model demonstrates the ability and potential of applying such deep learning techniques in the detection of adrenal lesions on CT scans. It also shows an opportunity to detect adrenal incidentalomas using deep learning.
+
+> An AI solution will allow for lesions to be detected more systematically and flagged for the reporting radiologist. In addition to enhanced patient safety, through minimising missed cases and variability in reporting, this is likely to be a cost-effective solution, saving clinician time.
+– Professor Fahmy Hanna, Professor of Endocrinology and Metabolism, Keele Medical School and University Hospitals of North Midlands NHS Trust
+
+
+### Who was involved?
+
+This project was a collaboration between the NHS AI Lab Skunkworks, within the Transformation Directorate at NHS England and NHS Improvement, and University Hospitals of North Midlands NHS Trust.
+
+[comment]: <> (The below header stops the title from being rendered (as mkdocs adds it to the page from the "title" attribute) - this way we can add it in the main.html, along with the summary.)
+#