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Zhang X, Zhang Y, Yang J, Du H. A prostate seed implantation robot system based on human-computer interactions: Augmented reality and voice control. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5947-5971. [PMID: 38872565 DOI: 10.3934/mbe.2024262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
The technology of robot-assisted prostate seed implantation has developed rapidly. However, during the process, there are some problems to be solved, such as non-intuitive visualization effects and complicated robot control. To improve the intelligence and visualization of the operation process, a voice control technology of prostate seed implantation robot in augmented reality environment was proposed. Initially, the MRI image of the prostate was denoised and segmented. The three-dimensional model of prostate and its surrounding tissues was reconstructed by surface rendering technology. Combined with holographic application program, the augmented reality system of prostate seed implantation was built. An improved singular value decomposition three-dimensional registration algorithm based on iterative closest point was proposed, and the results of three-dimensional registration experiments verified that the algorithm could effectively improve the three-dimensional registration accuracy. A fusion algorithm based on spectral subtraction and BP neural network was proposed. The experimental results showed that the average delay of the fusion algorithm was 1.314 s, and the overall response time of the integrated system was 1.5 s. The fusion algorithm could effectively improve the reliability of the voice control system, and the integrated system could meet the responsiveness requirements of prostate seed implantation.
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Affiliation(s)
- Xinran Zhang
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Harbin University of Science and Technology, Harbin 150080, China
| | - Yongde Zhang
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Harbin University of Science and Technology, Harbin 150080, China
- Foshan Baikang Robot Technology Co., Ltd., Foshan 528237, China
| | - Jianzhi Yang
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Harbin University of Science and Technology, Harbin 150080, China
| | - Haiyan Du
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Harbin University of Science and Technology, Harbin 150080, China
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Stenhouse K, Roumeliotis M, Ciunkiewicz P, Martell K, Quirk S, Banerjee R, Doll C, Phan T, Yanushkevich S, McGeachy P. Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapy. Brachytherapy 2024; 23:368-376. [PMID: 38538415 DOI: 10.1016/j.brachy.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 05/18/2024]
Abstract
PURPOSE To Demonstrate the clinical validation of a machine learning (ML) model for applicator and interstitial needle prediction in gynecologic brachytherapy through a prospective clinical study in a single institution. METHODS The study included cervical cancer patients receiving high-dose-rate brachytherapy using intracavitary (IC) or hybrid interstitial (IC/IS) applicators. For each patient, the primary radiation oncologist contoured the high-risk clinical target volume on a pre-brachytherapy MRI, indicated the approximate applicator location, and made a clinical determination of the first fraction applicator. A pre-trained ML model predicted the applicator and IC/IS needle arrangement using tumor geometry. Following the first fraction, ML and radiation oncologist predictions were compared and a replanning study determined the applicator providing optimal organ-at-risk (OAR) dosimetry. The ML-predicted applicator and needle arrangement and the clinical determination were compared to this dosimetric ground truth. RESULTS Ten patients were accrued from December 2020 to October 2022. Compared to the dosimetrically optimal applicator, both the radiation oncologist and ML had an accuracy of 70%. ML demonstrated better identification of patients requiring IC/IS applicators and provided balanced IC and IC/IS predictions. The needle selection model achieved an average accuracy of 82.5%. ML-predicted needle arrangements matched or improved plan quality when compared to clinically selected arrangements. Overall, ML predictions led to an average total improvement of 2.0 Gy to OAR doses over three treatment fractions when compared to clinical predictions. CONCLUSION In the context of a single institution study, the presented ML model demonstrates valuable decision-support for the applicator and needle selection process with the potential to provide improved dosimetry. Future work will include a multi-center study to assess generalizability.
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Affiliation(s)
- Kailyn Stenhouse
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada.
| | - Michael Roumeliotis
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD.
| | - Philip Ciunkiewicz
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Kevin Martell
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Sarah Quirk
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Radiation Oncology, Brigham and Women's Hospital, Boston, MA
| | - Robyn Banerjee
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Corinne Doll
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Tien Phan
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Svetlana Yanushkevich
- Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Philip McGeachy
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada; Department of Oncology, University of Calgary, Calgary, Alberta, Canada
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Zhao JZ, Ni R, Chow R, Rink A, Weersink R, Croke J, Raman S. Artificial intelligence applications in brachytherapy: A literature review. Brachytherapy 2023; 22:429-445. [PMID: 37248158 DOI: 10.1016/j.brachy.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/02/2023] [Accepted: 04/07/2023] [Indexed: 05/31/2023]
Abstract
PURPOSE Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field. METHODS AND MATERIALS We conducted a literature search in June 2022 on PubMed, Embase, and Cochrane for papers that proposed AI applications in brachytherapy. RESULTS A total of 80 papers satisfied inclusion/exclusion criteria. These papers were categorized as follows: segmentation (24), registration and image processing (6), preplanning (13), dose prediction and treatment planning (11), applicator/catheter/needle reconstruction (16), and quality assurance (10). AI techniques ranged from classical models such as support vector machines and decision tree-based learning to newer techniques such as U-Net and deep reinforcement learning, and were applied to facilitate small steps of a process (e.g., optimizing applicator selection) or even automate the entire step of the workflow (e.g., end-to-end preplanning). Many of these algorithms demonstrated human-level performance and offer significant improvements in speed. CONCLUSIONS AI has potential to augment, automate, and/or accelerate many steps of the brachytherapy workflow. We recommend that future studies adhere to standard reporting guidelines. We also stress the importance of using larger sample sizes and reporting results using clinically interpretable measures.
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Affiliation(s)
- Jonathan Zl Zhao
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Ruiyan Ni
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Ronald Chow
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Alexandra Rink
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Robert Weersink
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Jennifer Croke
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Srinivas Raman
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
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Raman S, Gold GE, Rosen MS, Sveinsson B. Automatic estimation of knee effusion from limited MRI data. Sci Rep 2022; 12:3155. [PMID: 35210490 PMCID: PMC8873489 DOI: 10.1038/s41598-022-07092-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 02/10/2022] [Indexed: 01/17/2023] Open
Abstract
Knee effusion is a common comorbidity in osteoarthritis. To quantify the amount of effusion, semi quantitative assessment scales have been developed that classify fluid levels on an integer scale from 0 to 3. In this work, we investigated the use of a neural network (NN) that used MRI Osteoarthritis Knee Scores effusion-synovitis (MOAKS-ES) values to distinguish physiologic fluid levels from higher fluid levels in MR images of the knee. We evaluate its effectiveness on low-resolution images to examine its potential in low-field, low-cost MRI. We created a dense NN (dNN) for detecting effusion, defined as a nonzero MOAKS-ES score, from MRI scans. Both the training and performance evaluation of the network were conducted using public radiological data from the Osteoarthritis Initiative (OAI). The model was trained using sagittal turbo-spin-echo (TSE) MR images from 1628 knees. The accuracy was compared to VGG16, a commonly used convolutional classification network. Robustness of the dNN was assessed by adding zero-mean Gaussian noise to the test images with a standard deviation of 5-30% of the maximum test data intensity. Also, inference was performed on a test data set of 163 knees, which includes a smaller test set of 36 knees that was also assessed by a musculoskeletal radiologist and the performance of the dNN and the radiologist compared. For the larger test data set, the dNN performed with an average accuracy of 62%. In addition, the network proved robust to noise, classifying the noisy images with minimal degradation to accuracy. When given MRI scans with 5% Gaussian noise, the network performed similarly, with an average accuracy of 61%. For the smaller 36-knee test data set, assessed both by the dNN and by a radiologist, the network performed better than the radiologist on average. Classifying knee effusion from low-resolution images with a similar accuracy as a human radiologist using neural networks is feasible, suggesting automatic assessment of images from low-cost, low-field scanners as a potentially useful assessment tool.
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Affiliation(s)
- Sandhya Raman
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Matthew S Rosen
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Bragi Sveinsson
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
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