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. 2024 Jun 27:13:100582.
doi: 10.1016/j.ejro.2024.100582. eCollection 2024 Dec.

Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation

Varatharajan Nainamalai  1 , Hemin Ali Qair  1 , Egidijus Pelanis  1   2 , Håvard Bjørke Jenssen  3 , Åsmund Avdem Fretland  1   4 , Bjørn Edwin  1   4 , Ole Jakob Elle  1   5 , Ilangko Balasingham  1   6
Affiliations

Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation

Varatharajan Nainamalai et al. Eur J Radiol Open. .

Abstract

Objective: Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data.

Methods: We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates. The algorithm creates directories based on keywords in new file names.

Results: Our work focuses on organizing computed tomography (CT), magnetic resonance (MR) images, patient clinical data, and segmented annotations. In addition, an AI model is used to generate different initial labels that can be edited manually to create ground truth labels. The manually verified ground truth labels are later included in the structured dataset using an automated algorithm for future research.

Conclusion: This is a workflow with an AI model trained on local hospital medical data with output based/adapted to the users and their preferences. The automated algorithms and AI model could be implemented inside a secondary secure environment in the hospital to produce inferences.

Keywords: Artificial intelligence; Electronic health records; Ground truth creation; Segmentation; Structured data.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Workflow representation of the proposed automated dataset/database creation of secured hospital data for artificial intelligence study.
Fig. 2
Fig. 2
Overview of directory of the structured dataset. Porto-denotes the Portal venous phase of CT image. T1-denotes the T1 sequence of MR images. File names with IM, LP, and HV denotes the image, liver parenchyma, and hepatic vein respectively.
Fig. 3
Fig. 3
Three different views of liver parenchyma of patient A. (a), (b), (c) are AI predictions. (e), (f), (g) are corresponding manual corrections.
Fig. 4
Fig. 4
Three different views of liver vessels of randomly selected patient. (a), (b), (c) are AI predictions. (e), (f), (g) are corresponding manual corrections.
See this image and copyright information in PMC

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