Project Summary and Publication
Artificial intelligence for detection of critical findings in head CT (CT Brain) scans
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Principal Investigator: Prof. Qi DOU, Department of Computer Science and Engineering, The Chinese University of Hong Kong
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Comparative Efficacy and Safety of Treatments in Intracerebral Bleeding during Direct Oral Anticoagulant Usage – a Population-based Study
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Principal Investigator: Prof. Bonaventure IP, Department of Medicine & Therapeutics, The Chinese University of Hong Kong
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Deep learning of combined clinical data and CT images in patients with chronic viral hepatitis – risk prediction and complication reduction to facilitate eliminating viral hepatitis by year 2030
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Principal Investigator: Prof. Pong-Chi YUEN, Department of Computer Science, Hong Kong Baptist University
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Early prediction of type 2 diabetes onset and progression of diabetic complications using machine learning and deep learning techniques
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Principal Investigator: Dr Lin YANG, School of Nursing, The Hong Kong Polytechnic University
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Towards reducing repeated hospital attendance and admissions for patients with low back pain using a machine learning approach
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Principal Investigator: Dr Kenny KWAN, Department of Orthopaedics & Traumatology, The University of Hong Kong
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Novel diagnostic blood biomarkers in Alzheimer's disease
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Principal Investigator: Dr You-qiang SONG, School of Biomedical Sciences, The University of Hong Kong
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Establish a blood stream infection complication prediction model to facilitate precision diagnosis and reduce unnecessary invasive investigations
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Principal Investigator: Prof. Christopher LAI, Department of Microbiology, The Chinese University of Hong Kong
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AI-CARE: Artificial Intelligence for Breast Cancer Diagnosis from High-Dimensional MR Images
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Principal Investigator: Dr Hao CHEN, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology
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A multimodal representation learning framework for chest X-ray classification via cross-modality supervision between images and free-text radiology reports
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Principal Investigator: Dr Lequan YU, Department of Statistics & Actuarial Science, The University of Hong Kong
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Joint Replacement Follow-up Image Analysis
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Principal Investigator: Prof. Elvis CHUI, Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong
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Automation of intracranial aneurysm rupture analysis from images and NCDs pre-screened liquid-biopsy results
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Principal Investigator: Prof. David LAM, Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology
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Thyroid Eye Disease in Hong Kong from Epidemiology to Disease Modelling – a territory-wide study from Hospital Authority Data Collaboration Lab (HADCL) in 2000-2017
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Principal Investigator: Dr Kelvin CHONG, Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong
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Investigation of chronic disease trajectory of patients and subsequent morbidity and mortality risk, and potential impact on healthcare utilization
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Principal Investigator: Prof. Eng-Kiong YEOH, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong
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Answering the "surprise question" by applying machine learning and deep learning algorithms to structured data and medical free text across the care continuum to identify end-of-life (EOL) care needs and service gaps
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Principal Investigator: Prof. Timothy KWOK, Department of Medicine & Therapeutics, The Chinese University of Hong Kong
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Falls after Hospital Discharge among Older Adults: Epidemiology and Healthcare Burden
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Principal Investigator: Dr Patsy CHAU, School of Nursing, The University of Hong Kong
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AI-X: Artificial Intelligence for Chest X-ray Analysis: Towards Large-scale Data Screening and Manipulation
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Principal Investigator: Prof. Pheng Ann HENG, Department of Computer Science and Engineering, The Chinese University of Hong Kong
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Deep learning of combined clinical data and CT images in patients with chronic viral hepatitis – risk prediction and complication reduction to facilitate eliminating viral hepatitis by year 2030
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Principal Investigator: Prof. Grace WONG, Department of Medicine & Therapeutics, The Chinese University of Hong Kong
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Combating osteoporotic fractures in Hong Kong with artificial intelligence in the era of big data
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Principal Investigator: Dr Janus WONG, Department of Orthopaedics & Traumatology, The University of Hong Kong
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Opportunistic Population Screening Using AI-generated Imaging Biomarkers
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Principal Investigator: Dr Vince VARDHANABHUTI, Department of Diagnostic Radiology, The University of Hong Kong
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AI-Assisted Decision Support System for Cerebral Aneurysm Prediction and Treatment – disease de-confoundment & Liquid Biopsy Integrated Aneurysm Rupture Analysis
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Principal Investigator: Prof. David LAM, Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology
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Incidence of Major Adverse Cardiovascular Events in Rheumatoid Arthritis, Psoriatic Arthritis, and Axial Spondyloarthritis: a population-based cohort study
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Principal Investigator: Prof. Ho SO, Department of Medicine & Therapeutics, The Chinese University of Hong Kong
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Stroke Risk Prediction in Hong Kong: Using Hospital Authority Data Collaboration Laboratory
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Principal Investigator: Prof. Katie CHAN, Department of Biomedical Sciences and Electrical Engineering, The City University of Hong Kong
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Big Data Analytics Prediction Model of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer
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Principal Investigator: Dr Dominic FOO, Department of Surgery, The University of Hong Kong
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Medical Data Mining for Alzheimer’s Disease, Dementia, and Cancer in the Hong Kong Population
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Principal Investigator: Prof. Nancy IP, Division of Life Science, The Hong Kong University of Science and Technology
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Prediction of Adverse Drug Effects in Older Adults using Machine Learning Algorithms
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Principal Investigator: Prof. Elaine CHOW, Department of Medicine & Therapeutics, The Chinese University of Hong Kong
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The Development of Data-driven Algorithms for Optimising Resource Allocation across Ensembles of Primary, Acute and Subacute Care along Patient Journey
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Principal Investigator: Prof. Eman LEUNG, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong
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Stroke Risk Prediction in Hong Kong: using Hospital Authority Data Collaboration Laboratory (HADCL)
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Principal Investigator: Prof. Katie CHAN, Department of Biomedical Sciences and Electrical Engineering, City University of Hong Kong
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Deep learning of CT images in patients with chronic viral hepatitis – disease burden and resource allocation to facilitate eliminating viral hepatitis by year 2030
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Principal Investigator: Prof. Pong-Chi YUEN, Department of Computer Science, Hong Kong Baptist University
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Big Data Analytics Prediction Model of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer
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Principal Investigator: Dr Dominic FOO, Department of Surgery, The University of Hong Kong
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AI-Assisted Decision Support System for Cerebral Aneurysm Prediction and Treatment –Clinical Tests and Aneurysm Rupture Analysis
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Principal Investigator: Prof. David LAM, Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology
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Using big data to improve neonatal outcomes
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Principal Investigator: Prof. Simon LAM, Department of Paediatrics, The Chinese University of Hong Kong
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Burden of Streptococcus agalactiae (GBS) disease in Hong Kong
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Principal Investigator: Prof. Margaret IP, Department of Microbiology, The Chinese University of Hong Kong
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Integrative analysis of early risk prediction for Alzheimer's disease and dementia in the Hong Kong population
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Principal Investigator: Prof. Nancy IP, Division of Life Science, The Hong Kong University of Science and Technology
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Validation of an individualized risk prediction model based on a machine learning approach, for new-onset, progression and regression of chronic kidney disease in patients with type 2 diabetes in Hong Kong
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Principal Investigator: Dr Lin YANG, School of Nursing, The Hong Kong Polytechnic University
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Detection of osteoporosis and treatment response in preventing fragility fractures amongst the elderly using Artificial Intelligence and Big Data
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Principal Investigator: Prof. Michael KUO, Department of Diagnostic Radiology, The University of Hong Kong
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The Development of Data-driven Algorithms for Optimising Resource Allocation across Ensembles of Primary, Acute and Subacute Care along Patient Journey
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Principal Investigator: Prof. Eman LEUNG, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong
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Rapid Automated Computer Evaluation of CT Brain in Acute Ischemic Stroke and Intracranial Vessel Occlusion
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Principal Investigator: Dr Anderson TSANG, Department of Surgery, The University of Hong Kong
Project Summary:
Endovascular mechanical thrombectomy is now an established treatment for acute ischemic stroke caused by large vessel occlusion (LVO). As the majority of ischaemic stroke patients would first undergo plain Computed Tomography (CT) brain scan, it is imperative that signs of LVO on plain CT scans can be identified quickly and reliably so to initiate confirmatory investigations, clinical referral and proper treatment. The objective of the study is to develop a novel, rapid and automated computer algorithm capable of detecting and predicting signs and likelihood of LVO. The ultimate goal is to generate an effective, reliable and locally relevant platform for triaging acute stroke patients.
Publication:
- You, Jia, Yu, Philip L.H, Tsang, Anderson C.O, Tsui, Eva L.H, Woo, Pauline P.S, Lui, Carrie S.M, Leung, Gilberto K.K, Mahboobani, Neeraj, Chu, Chi-yeung, Chong, Wing-ho, Poon, Wai-lun (2021). 3D dissimilar-siamese-u-net for hyperdense Middle cerebral artery sign segmentation. Computerized Medical Imaging and Graphics, 90, 101898. (Link)
- You, Jia, Tsang, Anderson C O, Yu, Philip L H, Tsui, Eva L H, Woo, Pauline P S, Lui, Carrie S M, & Leung, Gilberto K K. (2020). Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke. Frontiers in Neuroinformatics, 14, 13. (Link)
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Disease Burden of Chronic Viral Hepatitis in Hong Kong – Prediction Models Based on Clinical, Laboratory and Radiological Parameters
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Principal Investigator: Prof. Grace WONG, Department of Medicine & Therapeutics, The Chinese University of Hong Kong
Project Summary:
A comprehensive review of the secular trend and current situation of chronic viral hepatitis would provide pivotal data to the Government and the Steering Committee to formulate strategies to effectively prevent and control viral hepatitis to guide the strategies to achieve the goals set by the World Health Organization. This project reports the increasing uptake of antiviral treatment in patients with chronic viral hepatitis from 2000 to 2018. The researchers are developing a machine learning model as the risk stratification tool and to predict important clinical outcomes. A machine-learning model based on clinical parameters available in the Clinical Management System (CMS) are being developed to facilitate the clinical assessment and risk stratification of liver complications including liver cancer in patients with chronic viral hepatitis. The machine learning computer-aided diagnosis for liver nodules are being optimised. The findings will provide important data to address the request from the Chief Executive's 2017 Policy Address to formulate strategies to effectively prevent and control viral hepatitis, in particular for the planning of upcoming strategies to eradicate chronic viral hepatitis by year 2030 according to the advocate of World Health Organization.
Publication:
- Wong GL, Tse YK, Yuen BW, Yip TC, Wong VW, Chan HL, Yuen PC. Secular trend of treatment uptake in patients with chronic viral hepatitis: a territory-wide study of 120,279 patients with data from HADCL from year 2000 to 2017. Hepatol Int 2020;14(Suppl 1):A637. (Link)
- Wong GL, Tan Q, Tse YK, Yang BY, Yip TC, Yin C, Hui VW, Yuen BW, Chan HL, Wong VW, Yuen PC. Machine learning models to predict hepatocellular carcinoma in patients with chronic viral hepatitis – a territory-wide study from hospital authority data collaboration lab (HADCL) in 2000-2017. Hepatology 2020;72(Supp 1):466A. (Link)
- Wong GL, Yin C, Tse YK, Lyu F, Yip TC, Hui VW, Chan HL, Wong VW, Yuen PC. Deep learning of CT images in patients with chronic viral hepatitis – a territory-wide study with data from HADCL from year 2000 to 2017. Hepatol Int 2021;15(Suppl 1):A74. (p.123 of 306) (Link)
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Automatic Hip Fractures Detection Using Deep Learning
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Principal Investigator: Prof. Michael KUO, Department of Diagnostic Radiology, The University of Hong Kong
Project Summary: Coming soon.
Publication: Coming soon.
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AI Assisted Decision Support System for Cerebral Aneurysm Risk (CARI) Assessment and Treatment
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Principal Investigator: Prof. David LAM, Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology
Project Summary:
Mortality and morbidity rates (MMR) are over 70% for patients admitted into emergency with a ruptured aneurysm. MMR is less than 7% if the patients are admitted before rupture. Currently, more than 200,000 people in Hong Kong are at risk of cerebral aneurysm rupture. Cerebral Aneurysm Risk Index (CARI) developed in this project can help. Early detection of cerebral aneurysm using CARI can reduce MMR to less than 7% and facilitate preventive treatment before rupture. Development of Phase I CARI is led by HKUST and KWH in collaboration with HADCL.
- CARI provides quantified aneurysm risk index to facilitate and support clinical decisions
- Helps identify high-risk patients for treatment before rupture
- Decision support to lower MMR of high-risk patients
Publication: Coming soon.
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The Development of Data-driven Algorithms for Optimising Resource Allocation across Ensembles of Primary, Acute and Subacute Care along the Patient Journey
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Principal Investigator: Prof. Eman LEUNG, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong
Project Summary: Coming soon.
Publication: Coming soon.
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Deep Learning in the Assessment of Non-contrast CT Brain Scans for Intracranial Haemorrhage
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Principal Investigator: Dr Jill ABRIGO, Department of Imaging & Interventional Radiology, The Chinese University of Hong Kong
Project Summary:
Across HA hospitals, a high volume of brain CT scans are performed daily in the accident & emergency departments (AEDs) for the assessment of patients with acute conditions such as trauma, sudden headache, dizziness or stroke. One of the critical findings in CT scans is bleeding in the brain or “intracranial haemorrhage” (ICH). ICH requires timely identification as its presence and type determine the next step in the patient's clinical management. Conversely, its absence is equally important in the treatment of acute stroke and safe discharge of patients.
CT scans are 3D stacks of 20-300 images of the brain, and may be comprised of thick slices (~5mm) or thin slices (<1mm). Radiologists are specialised in interpreting CT scans but in practice, when patients undergo brain CT scans in the AED, initial interpretation of these images relies heavily on frontline emergency physicians, who may have limited experience and time in reading CT scans. Several studies have demonstrated that detection of ICH on brain CT scans using artificial intelligence (AI) can be accurate and useful for triage purposes, and for our purpose can potentially assist emergency physicians. In this project, we used a convolutional neural network - based multi-label classification task to develop a deep learning model for ICH detection. The model was first trained on an international publicly available dataset of over 670,000 expert-labeled CT slices, and independently tested on CT scans from the HA Data Collaboration Laboratory which constituted “unseen data”. The model output provided (1) an overall probability of ICH per CT scan and nominated (2) 5 potential ICH-positive CT slices for human cross reference.
The multi-center CT scans from HA Data Collaboration Laboratory (HADCL) were retrieved. The CT scans comprised of thin- and thick slices and were expert-labelled by radiologists for the presence or absence of ICH. Diagnostic accuracy of the model was determined using Area under the receiver operating characteristics curve (AUC) analysis; AUC ranges from 0 to 1 where 0 indicates a perfectly incorrect classification and 1 indicates perfectly correct classification. Since the model provided an overall probability of ICH, we used a probability threshold at approximately 80% sensitivity for binary classification of the model result into ICH-positive or ICH-negative. The corresponding specificity, positive predictive value (PPV) and negative predictive value (NPV) were then calculated accordingly. We assessed the diagnostic performance at the CT scan-level with subgroup analysis for both thin and thick slice CT scans. We additionally performed post-hoc labelling of the model-nominated CT slices to assess diagnostic performance at the slice-level.
Results for CT scans with thin slices
There were 2397 CT scans with thin slices. Of these, 1329 were ICH-negative and 1068 were ICH-positive. The model showed AUC of 0.81 in this cohort. An ICH probability threshold of 35% provided sensitivity of 81%, with specificity, PPV and NPV of 60%, 72% and 72%, respectively.
In 261 CT scans with post-hoc labelling of 5 nominated slices (total: 1305 CT slices), AUC was 0.85. A threshold of 45% provided sensitivity of 80%, with specificity, PPV and NPV of 74%, 87% and 63%, respectively.
Results for CT scans with thick slices
There were 389 CT scans with thick slices. Of these, 46 were ICH-negative and 343 were ICH-positive. The model showed AUC of 0.83 in this cohort. An ICH probability threshold of 50% provided sensitivity of 80%, with specificity, PPV and NPV of 67%, 95% and 31% respectively.
In 308 CT scans with post-hoc labelling of 5 nominated slices (total: 1540 CT slices), AUC was 0.88. A threshold of 60% provided sensitivity of 80%, with specificity, PPV and NPV of 83%, 91% and 65%, respectively.
Comparison of CT scans with thin and thick slices
There were 382 CT scans with both thin and thick slices. Of these, 342 were ICH-positive and 40 were ICH-negative. AUC’s were comparable (0.80 for thin slice CT scans vs. 0.82 for thick slice CT scans). At 80% sensitivity, the specificity/PPV/NPV were 53% / 93% / 23% for CT scans with thin slices and 65% / 95% / 27% for CT scans with thick slices.
Our results show that a deep learning tool allows detection of ICH in multi-center CT scans in the HADCL, therefore has potential to be used under the HA setting. Diagnostic accuracy appears comparable between thin and thick slice CT scans. However, for the intended purpose in the AED, further model improvement should be directed towards more accurate detection of ICH on CT scans with thick slices, and in facilitating cross checking of AI results by emergency physicians.
Publication: Coming soon.