Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects

Hansen, Ulrik Stig; Landau, Eric; Patel, Mehul; Hayee, Bu’Hussain

doi:10.1101/2020.09.11.20192500

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Fujifilm EC760 zoom-type colonoscopes were used throughout, restricted by the study intent. Video clips were extracted, unrestricted to anatomy, by a researcher blinded to patient details, disease extent or severity, managed and then scored using a previously-described methodology to prepare videos on a proprietary platform (Cord Vision, Cord Technologies, UK) 17 .…”

Section: Methodsmentioning

confidence: 99%

Rapid development of accurate artificial intelligence scoring for colitis disease activity using applied data science techniques

Patel

Gulati

Iqbal³

et al. 2022

Endosc Int Open

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Background and study aims Scoring endoscopic disease activity in colitis represents a complex task for artificial intelligence (AI), but is seen as a worthwhile goal for clinical and research use cases. To date, development attempts have relied on large datasets, achieving reasonable results when comparing normal to active inflammation, but not when generating subscores for the Mayo Endoscopic Score (MES) or ulcerative colitis endoscopic index of severity (UCEIS). Patients and methods Using a multi-task learning framework, with frame-by-frame analysis, we developed a machine-learning algorithm (MLA) for UCEIS trained on just 38,124 frames (73 patients with biopsy-proven ulcerative colitis). Scores generated by the MLA were compared to consensus scores from three independent human reviewers. Results Accuracy and agreement (kappa) were calculated for the following differentiation tasks: (1) normal mucosa vs active inflammation (UCEIS 0 vs ≥ 1; accuracy 0.90, κ = 0.90); (2) mild inflammation vs moderate-severe (UCEIS 0–3 vs ≥ 4; accuracy 0.98, κ = 0.96); (3) generating total UCEIS score (κ = 0.92). Agreement for UCEIS subdomains was also high (κ = 0.80, 0.83 and 0.88 for vascular pattern, bleeding and erosions respectively). Conclusions We have demonstrated that, using modified data science techniques and a relatively smaller datasets, it is possible to achieve high levels of accuracy and agreement with human reviewers (in some cases near-perfect), for AI in colitis scoring. Further work will focus on refining this technique, but we hope that it can be used in other tasks to facilitate faster development.

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Section: Methodsmentioning

confidence: 99%

Rapid development of accurate artificial intelligence scoring for colitis disease activity using applied data science techniques

Patel

Gulati

Iqbal³

et al. 2022

Endosc Int Open

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“…의료 영상을 사용한다는 특수함으로 인해 타 분야에 비해 소프트웨어 선택의 폭이 다소 좁다. Table 2 에는 의료 영상을 지원하는 무료 및 유료 레이블링 소프트웨어들을 보여준다( 21 22 23 24 25 ). 무료로 제공되는 소프트웨어 중 가장 대표적인 것은 NIH에서 제공하는 ImageJ 소프트웨어이다.…”

Section: 데이터 수집unclassified

Development of an Optimized Deep Learning Model for Medical Imaging

김

2020

J Korean Soc Radiol

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Deep learning has recently become one of the most actively researched technologies in the field of medical imaging. The availability of sufficient data and the latest advances in algorithms are important factors that influence the development of deep learning models. However, several other factors should be considered in developing an optimal generalized deep learning model. All the steps, including data collection, labeling, and pre-processing and model training, validation, and complexity can affect the performance of deep learning models. Therefore, appropriate optimization methods should be considered for each step during the development of a deep learning model. In this review, we discuss the important factors to be considered for the optimal development of deep learning models.

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Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects

Cited by 2 publications

References 7 publications

Rapid development of accurate artificial intelligence scoring for colitis disease activity using applied data science techniques

Rapid development of accurate artificial intelligence scoring for colitis disease activity using applied data science techniques

Development of an Optimized Deep Learning Model for Medical Imaging

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