Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans

Dyer, T. A.; Chawda, Sanjiv; Alkilani, Raed; Morgan, Tom Naunton; Hughes, Mike; Rasalingham, Simon

doi:10.1007/s00234-021-02826-4

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Traumatic ICH also makes a significant contribution to mortality. Every year, about 69 million people experience a traumatic brain injury (TBI) [2], of which about 5.48 million suffer from severe TBI with ICH [3]. Mortality in this group of patients is up to 90%.…”

Section: Discussionmentioning

confidence: 99%

Expanded Brain CT Dataset for the Development of AI Systems for Intracranial Hemorrhage Detection and Classification

Khoruzhaya,

Bobrovskaya,

Kozlov

et al. 2024

Data

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Intracranial hemorrhage (ICH) is a dangerous life-threatening condition leading to disability. Timely and high-quality diagnosis plays a huge role in the course and outcome of this disease. The gold standard in determining ICH is computed tomography. This method requires a prompt involvement of highly qualified personnel, which is not always possible, for example, in case of a staff shortage or increased workload. In such a situation, every minute counts, and time can be lost. The solution to this problem seems to be a set of diagnostic decisions, including the use of artificial intelligence, which will help to identify patients with ICH in a timely manner and provide prompt and quality medical care. However, the main obstacle to the development of artificial intelligence is a lack of high-quality datasets for training and testing. In this paper, we present a dataset including 800 brain CT scans consisting of multiple series of DICOM images with and without signs of ICH, enriched with clinical and technical parameters, as well as the methodology of its generation utilizing natural language processing tools. The dataset is publicly available, which contributes to increased competition in the development of artificial intelligence systems and their advancement and quality improvement.

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Section: Discussionmentioning

confidence: 99%

Expanded Brain CT Dataset for the Development of AI Systems for Intracranial Hemorrhage Detection and Classification

Khoruzhaya,

Bobrovskaya,

Kozlov

et al. 2024

Data

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“…22 Although some AI tools have been validated in the UK population, most have been in a single-centre setting with relatively small data sets. [23][24][25] The AI tools which have been developed using the largest data sets (above 100 000 scans) have originated from China and India 11 12 and have not been validated in the UK. The geographical setting is significant because early AI models' performance degraded sharply when tested on data from different populations, even from different hospitals within the same city, due to differences in patient characteristics, prevalence of abnormalities and imaging hardware.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study

Fu,

Novak,

Robert

et al. 2024

BMJ Open

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IntroductionA non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice.Methods and analysisA retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers’ performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty.Ethics and disseminationThe study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal.Trial registration numberNCT06018545.

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“…Artificial intelligence can eliminate the workload and pressure on emergency departments by excluding typical head computed tomography (CT) scans. 5 Algorithms that make triage or classifications such as intraparenchymal, intraventricular, extra-axial, or subarachnoid hemorrhages were designed with the help of machine learning (ML) for acute intracranial hemorrhages. 6 - 8 Automated segmentation and measurements of hematoma can be achieved with a non-contrast head CT scan.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Healthcare Competition (TEKNOFEST-2021): Stroke Data Set

Koç¹,

Sezer²,

Ozkaya³

et al. 2022

Eurasian J Med

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Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a non-disclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflecting various cases and problems. Especially, annotated data set by domain experts is more valuable.

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Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans

Cited by 6 publications

References 23 publications

Expanded Brain CT Dataset for the Development of AI Systems for Intracranial Hemorrhage Detection and Classification

Expanded Brain CT Dataset for the Development of AI Systems for Intracranial Hemorrhage Detection and Classification

AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study

Artificial Intelligence in Healthcare Competition (TEKNOFEST-2021): Stroke Data Set

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