A Medical Image Visualization Technique Assisted with AI-Based Haptic Feedback for Robotic Surgery and Healthcare

Minopoulos, Georgios; Memos, Vasileios A.; Stergiou, Konstantinos D.; Stergiou, Christos; Psannis, Kostas E.

doi:10.3390/app13063592

Cited by 21 publications

(8 citation statements)

References 18 publications

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“…The segmented structures (in this case, organs and their disorders) may be next applied to the development of the 3D virtual environment [141]. These 3D objects may be implemented through, for example, holograms displayed in a head-mounted display (HDM), such as mixed-reality glasses in medical diagnostics [150], preoperative imaging [151], surgical assistance [152,153], robotics surgery [154], and medical education [87,88]. However, the crucial issue is connected with the quality of the obtained segmented structures, and this process can be significantly accelerated and improved by the use of artificial intelligence.…”

Section: Neural Network and Learning Algorithms In The Medical Image ...mentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka,

Szczepanski,

Pregowska

2024

Electronics

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Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed.

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Section: Neural Network and Learning Algorithms In The Medical Image ...mentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka,

Szczepanski,

Pregowska

2024

Electronics

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“…The segmented structures (in this case organs and their disorders) may be next applied to the development of the 3D virtual environment [105]. These 3D objects may be implemented through for example, holograms displayed in the head-mounted display (HDMs) like Mixed Reality glasses in medical diagnostics [113], pre-operative imaging [114], surgical assistance [115,116], robotics surgery [117], and medical education [81,82]. However, the crucial issue is connected with the quality of obtained segmented structures, and this process can be significantly accelerated and improved by the use of Artificial Intelligence.…”

Section: Neural Network and Learning Algorithms In The Medical Image ...mentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Rudnicka,

Szczepanski,

Pregowska

2023

Preprint

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Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

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“…According to Saga et al [2], AI's precision and wide-ranging benefits have attracted extensive research interest. In medical contexts, AI has proven invaluable in managing large datasets, aiding in clinical decision-making, and enhancing the accuracy of medical practice [3][4][5][6][7][8][9]. Specifically, AI-powered tools have become integral in image-guided systems for surgical planning and simulation, leveraging advanced visualization technologies [10].…”

Section: Introductionmentioning

confidence: 99%

Automatization of CT Annotation: Combining AI Efficiency with Expert Precision

Edelmers,

Kazoka,

Bolocko

et al. 2024

Diagnostics

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The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum–coccyx complex. Patient selection was meticulously conducted, ensuring demographic balance in age and sex, and excluding scans with significant vertebral abnormalities to reduce confounding variables. This strategic selection bolstered the representativeness of our sample, thereby enhancing the external validity of our findings. Our workflow streamlined the segmentation process by eliminating the need for volume stitching, aligning seamlessly with the methodology we present. By leveraging AI, we have introduced a semi-automated annotation system that enables initial data labeling even by individuals without medical expertise. This phase is complemented by thorough manual validation against established anatomical standards, significantly reducing the time traditionally required for segmentation. This dual approach not only conserves resources but also expedites project timelines. While this method significantly advances radiological data annotation, it is not devoid of challenges, such as the necessity for manual validation by anatomically skilled personnel and reliance on specialized GPU hardware. Nonetheless, our methodology represents a substantial leap forward in medical data semantic segmentation, highlighting the potential of AI-driven approaches to revolutionize clinical and research practices in radiology.

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A Medical Image Visualization Technique Assisted with AI-Based Haptic Feedback for Robotic Surgery and Healthcare

Cited by 21 publications

References 18 publications

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Automatization of CT Annotation: Combining AI Efficiency with Expert Precision

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