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Vossough A, Khalili N, Familiar AM, Gandhi D, Viswanathan K, Tu W, Haldar D, Bagheri S, Anderson H, Haldar S, Storm PB, Resnick A, Ware JB, Nabavizadeh A, Fathi Kazerooni A. Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors. AJNR Am J Neuroradiol 2024; 45:1081-1089. [PMID: 38724204 PMCID: PMC11383404 DOI: 10.3174/ajnr.a8293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 03/01/2024] [Indexed: 08/11/2024]
Abstract
BACKGROUND AND PURPOSE Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is time-consuming and has high interoperator variability, underscoring the need for more efficient methods. After training, we compared 2 deep-learning-based 3D segmentation models, DeepMedic and nnU-Net, with pediatric-specific multi-institutional brain tumor data based on multiparametric MR images. MATERIALS AND METHODS Multiparametric preoperative MR imaging scans of 339 pediatric patients (n = 293 internal and n = 46 external cohorts) with a variety of tumor subtypes were preprocessed and manually segmented into 4 tumor subregions, ie, enhancing tumor, nonenhancing tumor, cystic components, and peritumoral edema. After training, performances of the 2 models on internal and external test sets were evaluated with reference to ground truth manual segmentations. Additionally, concordance was assessed by comparing the volume of the subregions as a percentage of the whole tumor between model predictions and ground truth segmentations using the Pearson or Spearman correlation coefficients and the Bland-Altman method. RESULTS The mean Dice score for nnU-Net internal test set was 0.9 (SD, 0.07) (median, 0.94) for whole tumor; 0.77 (SD, 0.29) for enhancing tumor; 0.66 (SD, 0.32) for nonenhancing tumor; 0.71 (SD, 0.33) for cystic components, and 0.71 (SD, 0.40) for peritumoral edema, respectively. For DeepMedic, the mean Dice scores were 0.82 (SD, 0.16) for whole tumor; 0.66 (SD, 0.32) for enhancing tumor; 0.48 (SD, 0.27) for nonenhancing tumor; 0.48 (SD, 0.36) for cystic components, and 0.19 (SD, 0.33) for peritumoral edema, respectively. Dice scores were significantly higher for nnU-Net (P ≤ .01). Correlation coefficients for tumor subregion percentage volumes were higher (0.98 versus 0.91 for enhancing tumor, 0.97 versus 0.75 for nonenhancing tumor, 0.98 versus 0.80 for cystic components, 0.95 versus 0.33 for peritumoral edema in the internal test set). Bland-Altman plots were better for nnU-Net compared with DeepMedic. External validation of the trained nnU-Net model on the multi-institutional Brain Tumor Segmentation Challenge in Pediatrics (BraTS-PEDs) 2023 data set revealed high generalization capability in the segmentation of whole tumor, tumor core (a combination of enhancing tumor, nonenhancing tumor, and cystic components), and enhancing tumor with mean Dice scores of 0.87 (SD, 0.13) (median, 0.91), 0.83 (SD, 0.18) (median, 0.89), and 0.48 (SD, 0.38) (median, 0.58), respectively. CONCLUSIONS The pediatric-specific data-trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.
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Affiliation(s)
- Arastoo Vossough
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Radiology (A.V., S.B., J.B.W., A.N.), University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Radiology (A.V.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Nastaran Khalili
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Ariana M Familiar
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Deep Gandhi
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Karthik Viswanathan
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Wenxin Tu
- College of Arts and Sciences (W.T.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Debanjan Haldar
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Sina Bagheri
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Radiology (A.V., S.B., J.B.W., A.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hannah Anderson
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Shuvanjan Haldar
- School of Engineering (S.H.), Rutgers University, New Brunswick, New Jersey
| | - Phillip B Storm
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Neurosurgery (P.B.S., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Adam Resnick
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Jeffrey B Ware
- Department of Radiology (A.V., S.B., J.B.W., A.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ali Nabavizadeh
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Radiology (A.V., S.B., J.B.W., A.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Anahita Fathi Kazerooni
- From the Center for Data Driven Discovery in Biomedicine (A.V., N.K., A.M.F., D.G., K.V., D.H., S.B., H.A., P.B.S., A.R., A.N., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Neurosurgery (P.B.S., A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Center for AI & Data Science for Integrated Diagnostics (A.F.K.), University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Biomedical Image Computing and Analytics (A.F.K.), University of Pennsylvania, Philadelphia, Pennsylvania
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Kumar K, Yeo AU, McIntosh L, Kron T, Wheeler G, Franich RD. Deep Learning Auto-Segmentation Network for Pediatric Computed Tomography Data Sets: Can We Extrapolate From Adults? Int J Radiat Oncol Biol Phys 2024; 119:1297-1306. [PMID: 38246249 DOI: 10.1016/j.ijrobp.2024.01.201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 12/10/2023] [Accepted: 01/07/2024] [Indexed: 01/23/2024]
Abstract
PURPOSE Artificial intelligence (AI)-based auto-segmentation models hold promise for enhanced efficiency and consistency in organ contouring for adaptive radiation therapy and radiation therapy planning. However, their performance on pediatric computed tomography (CT) data and cross-scanner compatibility remain unclear. This study aimed to evaluate the performance of AI-based auto-segmentation models trained on adult CT data when applied to pediatric data sets and explore the improvement in performance gained by including pediatric training data. It also examined their ability to accurately segment CT data acquired from different scanners. METHODS AND MATERIALS Using the nnU-Net framework, segmentation models were trained on data sets of adult, pediatric, and combined CT scans for 7 pelvic/thoracic organs. Each model was trained on 290 to 300 cases per category and organ. Training data sets included a combination of clinical data and several open repositories. The study incorporated a database of 459 pediatric (0-16 years) CT scans and 950 adults (>18 years), ensuring all scans had human expert ground-truth contours of the selected organs. Performance was evaluated based on Dice similarity coefficients (DSC) of the model-generated contours. RESULTS AI models trained exclusively on adult data underperformed on pediatric data, especially for the 0 to 2 age group: mean DSC was below 0.5 for the bladder and spleen. The addition of pediatric training data demonstrated significant improvement for all age groups, achieving a mean DSC of above 0.85 for all organs in every age group. Larger organs like the liver and kidneys maintained consistent performance for all models across age groups. No significant difference emerged in the cross-scanner performance evaluation, suggesting robust cross-scanner generalization. CONCLUSIONS For optimal segmentation across age groups, it is important to include pediatric data in the training of segmentation models. The successful cross-scanner generalization also supports the real-world clinical applicability of these AI models. This study emphasizes the significance of data set diversity in training robust AI systems for medical image interpretation tasks.
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Affiliation(s)
- Kartik Kumar
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Adam U Yeo
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; School of Science, RMIT University, Melbourne, Victoria, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Lachlan McIntosh
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Tomas Kron
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; School of Science, RMIT University, Melbourne, Victoria, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia
| | - Greg Wheeler
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Rick D Franich
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; School of Science, RMIT University, Melbourne, Victoria, Australia.
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El-Ghandour NMF. Commentary: Artificial Intelligence for Automatic Analysis of Shunt Treatment in Pre and Postsurgery Computed Tomography Brain Scans of iNPH Patients. Neurosurgery 2024:00006123-990000000-01191. [PMID: 38842345 DOI: 10.1227/neu.0000000000003026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 06/07/2024] Open
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Aminoshariae A, Nosrat A, Nagendrababu V, Dianat O, Mohammad-Rahimi H, O'Keefe AW, Setzer FC. Artificial Intelligence in Endodontic Education. J Endod 2024; 50:562-578. [PMID: 38387793 DOI: 10.1016/j.joen.2024.02.011] [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: 10/23/2023] [Revised: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
AIMS The future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could benefit from the implementation of artificial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients. METHODS An electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov. RESULTS The search identified 251 records, of which 35 were deemed relevant to artificial intelligence (AI) and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identified as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decision-making; 4) case difficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization. CONCLUSIONS AI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would benefit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI's limitations and challenges to overcome.
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Affiliation(s)
| | - Ali Nosrat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Venkateshbabu Nagendrababu
- Department of Preventive and Restorative Dentistry, University of Sharjah, College of Dental Medicine, Sharjah, United Arab Emirates
| | - Omid Dianat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | | | - Frank C Setzer
- Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Cekic E, Pinar E, Pinar M, Dagcinar A. Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images. World Neurosurg 2024; 182:e196-e204. [PMID: 38030068 DOI: 10.1016/j.wneu.2023.11.073] [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: 10/09/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
OBJECTIVE The primary aim of this research was to harness the capabilities of deep learning to enhance neurosurgical procedures, focusing on accurate tumor boundary delineation and classification. Through advanced diagnostic tools, we aimed to offer surgeons a more insightful perspective during surgeries, improving surgical outcomes and patient care. METHODS The study deployed the Mask R-convolutional neural network (CNN) architecture, leveraging its sophisticated features to process and analyze data from surgical microscope videos and preoperative magnetic resonance images. Resnet101 and Resnet50 backbone networks are used in the Mask R-CNN method, and experimental results are given. We subsequently tested its performance across various metrics, such as accuracy, precision, recall, dice coefficient (DICE), and Jaccard index. Deep learning models were trained from magnetic resonance imaging and surgical microscope images, and the classification result obtained for each patient was combined with the weighted average. RESULTS The algorithm exhibited remarkable capabilities in distinguishing among meningiomas, metastases, and high-grade glial tumors. Specifically, for the Mask R-CNN Resnet 101 architecture, precision, recall, DICE, and Jaccard index values were recorded as 96%, 93%, 91%, and 84%, respectively. Conversely, for the Mask R-CNN Resnet 50 architecture, these values stood at 94%, 89%, 89%, and 82%. Additionally, the model achieved an impressive DICE score range of 94%-95% and an accuracy of 98% in pathology estimation. CONCLUSIONS As illustrated in our study, the confluence of deep learning with neurosurgical procedures marks a transformative phase in medical science. The results are promising but underscore diverse data sets' significance for training and refining these deep learning models.
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Affiliation(s)
- Efecan Cekic
- Department of Neurosurgery, Polatli Duatepe State Hospital, Ankara, Turkey.
| | - Ertugrul Pinar
- Department of Neurosurgery, Private Pendik Yuzyil Hospital, İstanbul, Turkey
| | - Merve Pinar
- Department of Computer Engineering, Marmara University, İstanbul, Turkey
| | - Adnan Dagcinar
- Department of Neurosurgery, Marmara University, İstanbul, Turkey
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Tsai AY, Carter SR, Greene AC. Artificial intelligence in pediatric surgery. Semin Pediatr Surg 2024; 33:151390. [PMID: 38242061 DOI: 10.1016/j.sempedsurg.2024.151390] [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] [Indexed: 01/21/2024]
Abstract
Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and examine its relevance to pediatric surgery, covering its evolution, current state, and promising future. The various fields of AI are explored including machine learning and applications to predictive analytics and decision support in surgery, computer vision and image analysis in preoperative planning, image segmentation, surgical navigation, and finally, natural language processing assist in expediting clinical documentation, identification of clinical indications, quality improvement, outcome research, and other types of automated data extraction. The purpose of this review is to familiarize the pediatric surgical community with the rise of AI and highlight the ongoing advancements and challenges in its adoption, including data privacy, regulatory considerations, and the imperative for interdisciplinary collaboration. We hope this review serves as a comprehensive guide to AI's transformative influence on surgery, demonstrating its potential to enhance pediatric surgical patient outcomes, improve precision, and usher in a new era of surgical excellence.
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Affiliation(s)
- Anthony Y Tsai
- Division of Pediatric Surgery, Penn State Health Children's Hospital, 500 University Drive, Hershey, PA 17033, United States.
| | - Stewart R Carter
- Division of Pediatric Surgery, University of Louisville School of Medicine, Louisville, KY, United States
| | - Alicia C Greene
- Division of Pediatric Surgery, Penn State Health Children's Hospital, 500 University Drive, Hershey, PA 17033, United States
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Weninger L, Ecke J, Jütten K, Clusmann H, Wiesmann M, Merhof D, Na CH. Diffusion MRI anomaly detection in glioma patients. Sci Rep 2023; 13:20366. [PMID: 37990121 PMCID: PMC10663596 DOI: 10.1038/s41598-023-47563-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/15/2023] [Indexed: 11/23/2023] Open
Abstract
Diffusion-MRI (dMRI) measures molecular diffusion, which allows to characterize microstructural properties of the human brain. Gliomas strongly alter these microstructural properties. Delineation of brain tumors currently mainly relies on conventional MRI-techniques, which are, however, known to underestimate tumor volumes in diffusely infiltrating glioma. We hypothesized that dMRI is well suited for tumor delineation, and developed two different deep-learning approaches. The first diffusion-anomaly detection architecture is a denoising autoencoder, the second consists of a reconstruction and a discrimination network. Each model was exclusively trained on non-annotated dMRI of healthy subjects, and then applied on glioma patients' data. To validate these models, a state-of-the-art supervised tumor segmentation network was modified to generate groundtruth tumor volumes based on structural MRI. Compared to groundtruth segmentations, a dice score of 0.67 ± 0.2 was obtained. Further inspecting mismatches between diffusion-anomalous regions and groundtruth segmentations revealed, that these colocalized with lesions delineated only later on in structural MRI follow-up data, which were not visible at the initial time of recording. Anomaly-detection methods are suitable for tumor delineation in dMRI acquisitions, and may further enhance brain-imaging analysis by detection of occult tumor infiltration in glioma patients, which could improve prognostication of disease evolution and tumor treatment strategies.
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Affiliation(s)
- Leon Weninger
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- Department of Electrical Engineering, RWTH Aachen University, Aachen, Germany
| | - Jarek Ecke
- Department of Electrical Engineering, RWTH Aachen University, Aachen, Germany
| | - Kerstin Jütten
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany
| | - Hans Clusmann
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany
| | - Martin Wiesmann
- Department of Neuroradiology, RWTH Aachen University, Aachen, Germany
| | - Dorit Merhof
- Faculty of Informatics and Computer Science, University of Regensburg, Regensburg, Germany
- Frauenhofer-Institut für Digitale Medizin, MEVIS, Bremen, Germany
| | - Chuh-Hyoun Na
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany.
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany.
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Tozzi AE, Croci I, Voicu P, Dotta F, Colafati GS, Carai A, Fabozzi F, Lacanna G, Premuselli R, Mastronuzzi A. A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability. Front Oncol 2023; 13:1285775. [PMID: 38016063 PMCID: PMC10646175 DOI: 10.3389/fonc.2023.1285775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/16/2023] [Indexed: 11/30/2023] Open
Abstract
Introduction Europe works to improve cancer management through the use of artificialintelligence (AI), and there is a need to accelerate the development of AI applications for childhood cancer. However, the current strategies used for algorithm development in childhood cancer may have bias and limited generalizability. This study reviewed existing publications on AI tools for pediatric brain tumors, Europe's most common type of childhood solid tumor, to examine the data sources for developing AI tools. Methods We performed a bibliometric analysis of the publications on AI tools for pediatric brain tumors, and we examined the type of data used, data sources, and geographic location of cohorts to evaluate the generalizability of the algorithms. Results We screened 10503 publications, and we selected 45. A total of 34/45 publications developing AI tools focused on glial tumors, while 35/45 used MRI as a source of information to predict the classification and prognosis. The median number of patients for algorithm development was 89 for single-center studies and 120 for multicenter studies. A total of 17/45 publications used pediatric datasets from the UK. Discussion Since the development of AI tools for pediatric brain tumors is still in its infancy, there is a need to support data exchange and collaboration between centers to increase the number of patients used for algorithm training and improve their generalizability. To this end, there is a need for increased data exchange and collaboration between centers and to explore the applicability of decentralized privacy-preserving technologies consistent with the General Data Protection Regulation (GDPR). This is particularly important in light of using the European Health Data Space and international collaborations.
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Affiliation(s)
- Alberto Eugenio Tozzi
- Predictive and Preventive Medicine Research Unit, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Ileana Croci
- Predictive and Preventive Medicine Research Unit, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Paul Voicu
- Department of Neuroscience and Imaging, “SS Annunziata” Hospital, “G. D’Annunzio” University, Chieti, Italy
| | - Francesco Dotta
- Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | | | - Andrea Carai
- Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Francesco Fabozzi
- Department of Hematology/Oncology, Cell and Gene Therapy, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Giuseppe Lacanna
- Predictive and Preventive Medicine Research Unit, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Roberto Premuselli
- Department of Hematology/Oncology, Cell and Gene Therapy, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Angela Mastronuzzi
- Department of Hematology/Oncology, Cell and Gene Therapy, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
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Raghavendra U, Gudigar A, Paul A, Goutham TS, Inamdar MA, Hegde A, Devi A, Ooi CP, Deo RC, Barua PD, Molinari F, Ciaccio EJ, Acharya UR. Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives. Comput Biol Med 2023; 163:107063. [PMID: 37329621 DOI: 10.1016/j.compbiomed.2023.107063] [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: 12/26/2022] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 06/19/2023]
Abstract
A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.
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Affiliation(s)
- U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | - Aritra Paul
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - T S Goutham
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Ajay Hegde
- Consultant Neurosurgeon Manipal Hospitals, Sarjapur Road, Bangalore, India
| | - Aruna Devi
- School of Education and Tertiary Access, University of the Sunshine Coast, Caboolture Campus, Australia
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore, 599494, Singapore
| | - Ravinesh C Deo
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW, 2010, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129, Torino, Italy
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY, 10032, USA
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, 860-8555, Japan
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Tanyel T, Nadarajan C, Duc NM, Keserci B. Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us? Cancers (Basel) 2023; 15:4015. [PMID: 37627043 PMCID: PMC10452543 DOI: 10.3390/cancers15164015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 07/22/2023] [Accepted: 08/02/2023] [Indexed: 08/27/2023] Open
Abstract
Machine learning (ML) models have become capable of making critical decisions on our behalf. Nevertheless, due to complexity of these models, interpreting their decisions can be challenging, and humans cannot always control them. This paper provides explanations of decisions made by ML models in diagnosing four types of posterior fossa tumors: medulloblastoma, ependymoma, pilocytic astrocytoma, and brainstem glioma. The proposed methodology involves data analysis using kernel density estimations with Gaussian distributions to examine individual MRI features, conducting an analysis on the relationships between these features, and performing a comprehensive analysis of ML model behavior. This approach offers a simple yet informative and reliable means of identifying and validating distinguishable MRI features for the diagnosis of pediatric brain tumors. By presenting a comprehensive analysis of the responses of the four pediatric tumor types to each other and to ML models in a single source, this study aims to bridge the knowledge gap in the existing literature concerning the relationship between ML and medical outcomes. The results highlight that employing a simplistic approach in the absence of very large datasets leads to significantly more pronounced and explainable outcomes, as expected. Additionally, the study also demonstrates that the pre-analysis results consistently align with the outputs of the ML models and the clinical findings reported in the existing literature.
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Affiliation(s)
- Toygar Tanyel
- Department of Computer Engineering, Yildiz Technical University, Istanbul 34349, Türkiye;
| | - Chandran Nadarajan
- Department of Radiology, Gleneagles Hospital Kota Kinabalu, Kota Kinabalu 88100, Sabah, Malaysia;
| | - Nguyen Minh Duc
- Department of Radiology, Pham Ngoc Thach University of Medicine, Ho Chi Minh City 700000, Vietnam;
| | - Bilgin Keserci
- Department of Biomedical Engineering, Yildiz Technical University, Istanbul 34349, Türkiye
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11
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Khan MA, Khan A, Alhaisoni M, Alqahtani A, Alsubai S, Alharbi M, Malik NA, Damaševičius R. Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2023; 33:572-587. [DOI: 10.1002/ima.22831] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/18/2022] [Indexed: 08/25/2024]
Abstract
AbstractIn the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion‐based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre‐trained CNN model named EfficientNetB0 is fine‐tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework.
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Affiliation(s)
| | - Awais Khan
- Department of Computer Science HITEC University Taxila Pakistan
| | - Majed Alhaisoni
- Computer Sciences Department, College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia
| | - Abdullah Alqahtani
- College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al‐Kharj Saudi Arabia
| | - Shtwai Alsubai
- College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al‐Kharj Saudi Arabia
| | - Meshal Alharbi
- Department of Computer Science, College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al‐Kharj Saudi Arabia
| | - Nazir Ahmed Malik
- Cyber Reconnaissance and Combat Lab Bahria University Islamabad Islamabad Pakistan
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12
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Alleman K, Knecht E, Huang J, Zhang L, Lam S, DeCuypere M. Multimodal Deep Learning-Based Prognostication in Glioma Patients: A Systematic Review. Cancers (Basel) 2023; 15:cancers15020545. [PMID: 36672494 PMCID: PMC9856816 DOI: 10.3390/cancers15020545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 01/05/2023] [Accepted: 01/08/2023] [Indexed: 01/18/2023] Open
Abstract
Malignant brain tumors pose a substantial burden on morbidity and mortality. As clinical data collection improves, along with the capacity to analyze it, novel predictive clinical tools may improve prognosis prediction. Deep learning (DL) holds promise for integrating clinical data of various modalities. A systematic review of the DL-based prognostication of gliomas was performed using the Embase (Elsevier), PubMed MEDLINE (National library of Medicine), and Scopus (Elsevier) databases, in accordance with PRISMA guidelines. All included studies focused on the prognostication of gliomas, and predicted overall survival (13 studies, 81%), overall survival as well as genotype (2 studies, 12.5%), and response to immunotherapy (1 study, 6.2%). Multimodal analyses were varied, with 6 studies (37.5%) combining MRI with clinical data; 6 studies (37.5%) integrating MRI with histologic, clinical, and biomarker data; 3 studies (18.8%) combining MRI with genomic data; and 1 study (6.2%) combining histologic imaging with clinical data. Studies that compared multimodal models to unimodal-only models demonstrated improved predictive performance. The risk of bias was mixed, most commonly due to inconsistent methodological reporting. Overall, the use of multimodal data in DL assessments of gliomas leads to a more accurate overall survival prediction. However, due to data limitations and a lack of transparency in model and code reporting, the full extent of multimodal DL as a resource for brain tumor patients has not yet been realized.
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Affiliation(s)
- Kaitlyn Alleman
- Chicago Medical School, Rosalind Franklin University of Science and Medicine, Chicago, IL 60064, USA
| | - Erik Knecht
- Chicago Medical School, Rosalind Franklin University of Science and Medicine, Chicago, IL 60064, USA
| | - Jonathan Huang
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Lu Zhang
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Sandi Lam
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Michael DeCuypere
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Correspondence:
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13
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Yang Y, Zhang Y, Li Y. Artificial intelligence applications in pediatric oncology diagnosis. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:157-169. [PMID: 36937318 PMCID: PMC10017189 DOI: 10.37349/etat.2023.00127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/30/2022] [Indexed: 03/04/2023] Open
Abstract
Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.
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Affiliation(s)
- Yuhan Yang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yimao Zhang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yuan Li
- Laboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
- Correspondence: Yuan Li, Laboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
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14
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Luo Y, Zhang S, Tan W, Lin G, Zhuang Y, Zeng H. The Diagnostic Efficiency of Quantitative Diffusion Weighted Imaging in Differentiating Medulloblastoma from Posterior Fossa Tumors: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2022; 12:diagnostics12112796. [PMID: 36428860 PMCID: PMC9689934 DOI: 10.3390/diagnostics12112796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/24/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022] Open
Abstract
Medulloblastoma (MB) is considered the most common and highly malignant posterior fossa tumor (PFT) in children. The accurate preoperative diagnosis of MB is beneficial in choosing the appropriate surgical methods and treatment strategies. Diffusion-weighted imaging (DWI) has improved the accuracy of differential diagnosis of posterior fossa tumors. Nonetheless, further studies are needed to confirm its value for clinical application. This study aimed to evaluate the performance of DWI in differentiating MB from other PFT. A literature search was conducted using databases PubMed, Embase, and Web of Science for studies reporting the diagnostic performance of DWI for PFT from January 2000 to January 2022. A bivariate random-effects model was employed to evaluate the pooled sensitivities and specificities. A univariable meta-regression analysis was used to assess relevant factors for heterogeneity, and subgroup analyses were performed. A total of 15 studies with 823 patients were eligible for data extraction. Overall pooled sensitivity and specificity of DWI were 0.94 (95% confident interval [CI]: 0.89-0.97) and 0.94 (95% CI: 0.90-0.96) respectively. The area under the curve (AUC) of DWI was 0.98 (95% CI: 0.96-0.99). Heterogeneity was found in the sensitivity (I2 = 62.59%) and the specificity (I2 = 35.94%). Magnetic field intensity, region of interest definition and DWI diagnostic parameters are the factors that affect the diagnostic performance of DWI. DWI has excellent diagnostic accuracy for differentiating MB from other PFT. Hence, it is necessary to set DWI as a routine examination sequence for posterior fossa tumors.
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Affiliation(s)
- Yi Luo
- Shantou University Medical College, 22 Xinling Road, Jinping District, Shantou 515041, China
- Department of Radiology, Shenzhen Children’s Hospital, 7019 Yitian Road, Futian District, Shenzhen 518038, China
| | - Siqi Zhang
- Shantou University Medical College, 22 Xinling Road, Jinping District, Shantou 515041, China
- Department of Radiology, Shenzhen Children’s Hospital, 7019 Yitian Road, Futian District, Shenzhen 518038, China
| | - Weiting Tan
- Shenzhen Children’s Hospital of China Medical University, 7019 Yitian Road, Futian District, Shenzhen 518038, China
| | - Guisen Lin
- Department of Radiology, Shenzhen Children’s Hospital, 7019 Yitian Road, Futian District, Shenzhen 518038, China
| | - Yijiang Zhuang
- Department of Radiology, Shenzhen Children’s Hospital, 7019 Yitian Road, Futian District, Shenzhen 518038, China
| | - Hongwu Zeng
- Department of Radiology, Shenzhen Children’s Hospital, 7019 Yitian Road, Futian District, Shenzhen 518038, China
- Correspondence:
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15
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Lima AA, Mridha MF, Das SC, Kabir MM, Islam MR, Watanobe Y. A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders. BIOLOGY 2022; 11:469. [PMID: 35336842 PMCID: PMC8945195 DOI: 10.3390/biology11030469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 01/19/2023]
Abstract
Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.
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Affiliation(s)
- Aklima Akter Lima
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (A.A.L.); (M.F.M.); (S.C.D.); (M.M.K.)
| | - M. Firoz Mridha
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (A.A.L.); (M.F.M.); (S.C.D.); (M.M.K.)
| | - Sujoy Chandra Das
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (A.A.L.); (M.F.M.); (S.C.D.); (M.M.K.)
| | - Muhammad Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (A.A.L.); (M.F.M.); (S.C.D.); (M.M.K.)
| | - Md. Rashedul Islam
- Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh
| | - Yutaka Watanobe
- Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan;
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16
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Danilov GV, Ishankulov TA, Kotik KV, Shifrin MA, Potapov AA. [Artificial intelligence technologies in clinical neurooncology]. ZHURNAL VOPROSY NEIROKHIRURGII IMENI N. N. BURDENKO 2022; 86:127-133. [PMID: 36534634 DOI: 10.17116/neiro202286061127] [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: 06/17/2023]
Abstract
Neurooncology in the 21st century is a complex discipline integrating achievements of fundamental and applied neurosciences. Complex processes and data in clinical neurooncology determine the necessity for advanced methods of mathematical modeling and predictive analytics to obtain new scientific knowledge. Such methods are currently being developed in computer science (artificial intelligence). This review is devoted to potential and range of possible applications of artificial intelligence technologies in neurooncology with a special emphasis on glial tumors. Our conclusions may be valid for other areas of clinical medicine.
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Affiliation(s)
- G V Danilov
- Burdenko Neurosurgical Center, Moscow, Russia
| | | | - K V Kotik
- Burdenko Neurosurgical Center, Moscow, Russia
| | - M A Shifrin
- Burdenko Neurosurgical Center, Moscow, Russia
| | - A A Potapov
- Burdenko Neurosurgical Center, Moscow, Russia
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