Application of artificial intelligence in identifying the extracapsular extension during 3D automatic augmented reality RARP: A pilot study

Background: The application of virtual and augmented reality technologies to orthopaedic surgery training and practice aims to increase the safety and accuracy of procedures and reducing complications and costs. The purpose of this systematic review is to summarise the present literature on this topic while providing a detailed analysis of current flaws and benefits. Methods: A comprehensive search on the PubMed, Cochrane, CINAHL, and Embase database was conducted from inception to February 2021. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to improve the reporting of the review. The Cochrane Risk of Bias Tool and the Methodological Index for Non-Randomized Studies (MINORS) was used to assess the quality and potential bias of the included randomized and non-randomized control trials, respectively. Results: Virtual reality has been proven revolutionary for both resident training and preoperative planning. Thanks to augmented reality, orthopaedic surgeons could carry out procedures faster and more accurately, improving overall safety. Artificial intelligence (AI) is a promising technology with limitless potential, but, nowadays, its use in orthopaedic surgery is limited to preoperative diagnosis. Conclusions: Extended reality technologies have the potential to reform orthopaedic training and practice, providing an opportunity for unidirectional growth towards a patient-centred approach.

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Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽

10.1055/a-1311-8570 ◽

2020 ◽

Author(s):

Alanna Ebigbo ◽

Robert Mendel ◽

Tobias Rückert ◽

Laurin Schuster ◽

Andreas Probst ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Pilot Study ◽

White Light ◽

Submucosal Invasion ◽

Learning System ◽

Endoscopic Images ◽

Barrett’S Cancer ◽

Significant Difference ◽

Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

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Augmented Reality in Robotic Assisted Orthopaedic Surgery: A Pilot Study

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2021.103841 ◽

2021 ◽

pp. 103841

Author(s):

Hisham Iqbal ◽

Fabio Tatti ◽

Ferdinando Rodriguez y Baena

Keyword(s):

Pilot Study ◽

Augmented Reality ◽

Orthopaedic Surgery ◽

Robotic Assisted

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Role of Virtual Reality (VR), Augmented Reality (AR) and Artificial Intelligence (AI) in tertiary education and research of orthodontics: An insight.

Seminars in Orthodontics ◽

10.1053/j.sodo.2021.05.003 ◽

2021 ◽

Author(s):

Narayan H. Gandedkar ◽

Matthew T. Wong ◽

M. Ali Darendeliler

Keyword(s):

Artificial Intelligence ◽

Virtual Reality ◽

Augmented Reality ◽

Tertiary Education

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Using Microsoft HoloLens to improve memory recall in anatomy and physiology: A pilot study to examine the efficacy of using augmented reality in education

Journal of Educational Technology Development and Exchange ◽

10.18785/jetde.1201.02 ◽

2019 ◽

Vol 12 (1) ◽

Cited By ~ 2

Author(s):

chen chen ◽

Lei Zhang ◽

Tony Luczak ◽

Eboni Smith ◽

Reuben F Burch

Keyword(s):

Pilot Study ◽

Augmented Reality ◽

Memory Recall ◽

Anatomy And Physiology ◽

Microsoft Hololens

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Artificial Intelligence Is a Promising Prospect for the Detection of Prostate Cancer Extracapsular Extension With Mp-mri: A Two-center Comparative Study

10.21203/rs.3.rs-298296/v1 ◽

2021 ◽

Author(s):

Ying Hou ◽

Yi-Hong Zhang ◽

Jie Bao ◽

Mei-Ling Bao ◽

Guang Yang ◽

...

Keyword(s):

Prostate Cancer ◽

Artificial Intelligence ◽

Paired Comparison ◽

Characteristic Curve ◽

Multiparametric Mri ◽

Training Data ◽

Urinary Continence ◽

Data Sets ◽

Extracapsular Extension ◽

Promising Alternative

Abstract Purpose: A balance between preserving urinary continence and achievement of negative margins is of clinical relevance while implementary difficulty. Preoperatively accurate detection of prostate cancer (PCa) extracapsular extension (ECE) is thus crucial for determining appropriate treatment options. We aimed to develop and clinically validate an artificial intelligence (AI)-assisted tool for the detection of ECE in patients with PCa using multiparametric MRI. Methods: 849 patients with localized PCa underwent multiparametric MRI before radical prostatectomy were retrospectively included from two medical centers. The AI tool was built on a ResNeXt network embedded with a spatial attention map of experts’ prior knowledges (PAGNet) from 596 training data sets. The tool was validated in 150 internal and 103 external data sets, respectively; and its clinical applicability was compared with expert-based interpretation and AI-expert interaction.Results: An index PAGNet model using a single-slice image yielded the highest areas under the receiver operating characteristic curve (AUC) of 0.857 (95% confidence interval [CI], 0.827-0.884), 0.807 (95% CI, 0.735-0.867) and 0.728 (95% CI, 0.631-0.811) in the training, internal test and external test cohorts, compared to the conventional ResNeXt networks. For experts, the inter-reader agreement was observed in only 437/849 (51.5%) patients with a Kappa value 0.343. And the performance of two experts (AUC, 0.632 to 0.741 vs 0.715 to 0.857) was lower (paired comparison, all p values < 0.05) than that of AI assessment. When expert’ interpretations were adjusted by the AI assessments, the performance of both two experts was improved.Conclusion: Our AI tool, showing improved accuracy, offers a promising alternative to human experts for imaging staging of PCa ECE using multiparametric MRI.

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