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Usuff R, Kothandapani S, Rangan R, Dhatchnamurthy S. Enhancing radiographic image interpretation: WARES-PRS model for knee bone tumour detection. NETWORK (BRISTOL, ENGLAND) 2024:1-31. [PMID: 38932464 DOI: 10.1080/0954898x.2024.2357660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024]
Abstract
The early diagnosis of tumour is significant in biomedical research field to lower the severity level and restrict the process extension from cancer. Moreover, the detection of early sign of cancer is undertaken with extensive research efforts that dedicated to the disclosure and recognition of tumours. However, the limited data size as well as diverse appearance of images lowered the detection performance and failed to detect complex stage of tumour. So to solve these issues, a Weighted Adaptive Random Ensemble Support Vector-based Partial Reinforcement Search (WARES-PRS) algorithm is proposed that detected bone lesions accurately and also predicted the severity level stage efficiently. Further, the detection is performed with varied stages to diminish the presence of noise and undertaken effective classification. The performance is validated with CNUH dataset that enhanced image pre-processing tasks. Despite the proposed method uncover the mutual relationships between each pixel's local texture and the overall image's global context. The detection and classification efficiency is validated with various measures and the experimental results revealed that the detection accuracy is enhanced for the proposed approach by 98.5%. The outcomes of our study have exhibited a substantial contribution to assisting physicians in the detection of knee bone tumours.
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Affiliation(s)
- Rahamathunnisa Usuff
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sudhakar Kothandapani
- Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India
| | - Rajesh Rangan
- Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
| | - Saravanan Dhatchnamurthy
- Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India
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Xiao T, Kong S, Zhang Z, Hua D, Liu F. A review of big data technology and its application in cancer care. Comput Biol Med 2024; 176:108577. [PMID: 38739981 DOI: 10.1016/j.compbiomed.2024.108577] [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: 11/19/2023] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/16/2024]
Abstract
The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.
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Affiliation(s)
- Tianyun Xiao
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Shanshan Kong
- College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
| | - Zichen Zhang
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Dianbo Hua
- Beijing Sitairui Cancer Data Analysis Joint Laboratory, Beijing, 101149, China
| | - Fengchun Liu
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China; Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, Hebei, China; Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, Hebei, China
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3
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Jan F, Rahman A, Busaleh R, Alwarthan H, Aljaser S, Al-Towailib S, Alshammari S, Alhindi KR, Almogbil A, Bubshait DA, Ahmed MIB. Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach. J Imaging 2023; 9:242. [PMID: 37998088 PMCID: PMC10672484 DOI: 10.3390/jimaging9110242] [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: 09/30/2023] [Revised: 10/25/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023] Open
Abstract
Developmental dysplasia of the hip (DDH) is a disorder characterized by abnormal hip development that frequently manifests in infancy and early childhood. Preventing DDH from occurring relies on a timely and accurate diagnosis, which requires careful assessment by medical specialists during early X-ray scans. However, this process can be challenging for medical personnel to achieve without proper training. To address this challenge, we propose a computational framework to detect DDH in pelvic X-ray imaging of infants that utilizes a pipelined deep learning-based technique consisting of two stages: instance segmentation and keypoint detection models to measure acetabular index angle and assess DDH affliction in the presented case. The main aim of this process is to provide an objective and unified approach to DDH diagnosis. The model achieved an average pixel error of 2.862 ± 2.392 and an error range of 2.402 ± 1.963° for the acetabular angle measurement relative to the ground truth annotation. Ultimately, the deep-learning model will be integrated into the fully developed mobile application to make it easily accessible for medical specialists to test and evaluate. This will reduce the burden on medical specialists while providing an accurate and explainable DDH diagnosis for infants, thereby increasing their chances of successful treatment and recovery.
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Affiliation(s)
- Farmanullah Jan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Atta Rahman
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Roaa Busaleh
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Haya Alwarthan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Samar Aljaser
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Sukainah Al-Towailib
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Safiyah Alshammari
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Khadeejah Rasheed Alhindi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Asrar Almogbil
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Dalal A. Bubshait
- Department of Orthopedics, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Mohammed Imran Basheer Ahmed
- Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
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Hashemi M, Razzazan M, Bagheri M, Asadi S, Jamali B, Khalafi M, Azimi A, Rad S, Behroozaghdam M, Nabavi N, Rashidi M, Dehkhoda F, Taheriazam A, Entezari M. Versatile function of AMPK signaling in osteosarcoma: An old player with new emerging carcinogenic functions. Pathol Res Pract 2023; 251:154849. [PMID: 37837858 DOI: 10.1016/j.prp.2023.154849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/16/2023]
Abstract
AMP-activated protein kinase (AMPK) signaling has a versatile role in Osteosarcoma (OS), an aggressive bone malignancy with a poor prognosis, particularly in cases that have metastasized or recurred. This review explores the regulatory mechanisms, functional roles, and therapeutic applications of AMPK signaling in OS. It focuses on the molecular activation of AMPK and its interactions with cellular processes like proliferation, apoptosis, and metabolism. The uncertain role of AMPK in cancer is also discussed, highlighting its potential as both a tumor suppressor and a contributor to carcinogenesis. The therapeutic potential of targeting AMPK signaling in OS treatment is examined, including direct and indirect activators like metformin, A-769662, resveratrol, and salicylate. Further research is needed to determine dosing, toxicities, and molecular mechanisms responsible for the anti-osteosarcoma effects of these compounds. This review underscores the complex involvement of AMPK signaling in OS and emphasizes the need for a comprehensive understanding of its molecular mechanisms. By elucidating the role of AMPK in OS, the aim is to pave the way for innovative therapeutic approaches that target this pathway, ultimately improving the prognosis and quality of life for OS patients.
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Affiliation(s)
- Mehrdad Hashemi
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Mehrnaz Razzazan
- Medical Student, Student Research Committee, Golestan University of Medical Sciences, Gorgan, Iran
| | - Maryam Bagheri
- Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Saba Asadi
- Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Behdokht Jamali
- Department of Microbiology and Genetics, Kherad Institute of Higher Education, Bushehr, lran
| | - Maryam Khalafi
- Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Department of Genetics,Faculty of Medicine, Islamic Azad University, Kish International Branch, Kish, Iran
| | - Abolfazl Azimi
- Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Department of Genetics,Faculty of Medicine, Islamic Azad University, Kish International Branch, Kish, Iran
| | - Sepideh Rad
- Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Department of Genetics,Faculty of Medicine, Islamic Azad University, Kish International Branch, Kish, Iran
| | - Mitra Behroozaghdam
- Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Noushin Nabavi
- Department of Urologic Sciences and Vancouver Prostate Centre, University of British Columbia, Vancouver, BC V6H3Z6, Canada
| | - Mohsen Rashidi
- The Health of Plant and Livestock Products Research Center, Mazandaran University of Medical Sciences, Sari, Iran; Department Pharmacology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran.
| | - Farshid Dehkhoda
- Department of Orthopedics, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Afshin Taheriazam
- Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Department of Orthopedics, Faculty of Medicine, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| | - Maliheh Entezari
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
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Li Y, Dong B, Yuan P. The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis. Front Oncol 2023; 13:1207175. [PMID: 37746301 PMCID: PMC10513372 DOI: 10.3389/fonc.2023.1207175] [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: 04/17/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Background Malignant bone tumors are a type of cancer with varying malignancy and prognosis. Accurate diagnosis and classification are crucial for treatment and prognosis assessment. Machine learning has been introduced for early differential diagnosis of malignant bone tumors, but its performance is controversial. This systematic review and meta-analysis aims to explore the diagnostic value of machine learning for malignant bone tumors. Methods PubMed, Embase, Cochrane Library, and Web of Science were searched for literature on machine learning in the differential diagnosis of malignant bone tumors up to October 31, 2022. The risk of bias assessment was conducted using QUADAS-2. A bivariate mixed-effects model was used for meta-analysis, with subgroup analyses by machine learning methods and modeling approaches. Results The inclusion comprised 31 publications with 382,371 patients, including 141,315 with malignant bone tumors. Meta-analysis results showed machine learning sensitivity and specificity of 0.87 [95% CI: 0.81,0.91] and 0.91 [95% CI: 0.86,0.94] in the training set, and 0.83 [95% CI: 0.74,0.89] and 0.87 [95% CI: 0.79,0.92] in the validation set. Subgroup analysis revealed MRI-based radiomics was the most common approach, with sensitivity and specificity of 0.85 [95% CI: 0.74,0.91] and 0.87 [95% CI: 0.81,0.91] in the training set, and 0.79 [95% CI: 0.70,0.86] and 0.79 [95% CI: 0.70,0.86] in the validation set. Convolutional neural networks were the most common model type, with sensitivity and specificity of 0.86 [95% CI: 0.72,0.94] and 0.92 [95% CI: 0.82,0.97] in the training set, and 0.87 [95% CI: 0.51,0.98] and 0.87 [95% CI: 0.69,0.96] in the validation set. Conclusion Machine learning is mainly applied in radiomics for diagnosing malignant bone tumors, showing desirable diagnostic performance. Machine learning can be an early adjunctive diagnostic method but requires further research and validation to determine its practical efficiency and clinical application prospects. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023387057.
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Affiliation(s)
| | - Bo Dong
- Department of Orthopedics, Xi’an Honghui Hospital, Xi’an Jiaotong University, Xi’an Shaanxi, China
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Vaiyapuri T, Jothi A, Narayanasamy K, Kamatchi K, Kadry S, Kim J. Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model. Cancers (Basel) 2022; 14:cancers14246066. [PMID: 36551552 PMCID: PMC9776881 DOI: 10.3390/cancers14246066] [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: 11/12/2022] [Revised: 11/30/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022] Open
Abstract
Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique's goal is to identify osteosarcoma's existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models.
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Affiliation(s)
- Thavavel Vaiyapuri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 16278, Saudi Arabia
| | - Akshya Jothi
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kancheepuram 603203, India
| | - Kanagaraj Narayanasamy
- Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore 641021, India
| | - Kartheeban Kamatchi
- Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil 626126, India
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
| | - Jungeun Kim
- Department of Software, Kongju National University, Cheonan 31080, Republic of Korea
- Correspondence:
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Nasir MU, Khan S, Mehmood S, Khan MA, Zubair M, Hwang SO. Network Meddling Detection Using Machine Learning Empowered with Blockchain Technology. SENSORS (BASEL, SWITZERLAND) 2022; 22:6755. [PMID: 36146104 PMCID: PMC9500681 DOI: 10.3390/s22186755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/01/2022] [Accepted: 09/04/2022] [Indexed: 06/16/2023]
Abstract
The study presents a framework to analyze and detect meddling in real-time network data and identify numerous meddling patterns that may be harmful to various communication means, academic institutes, and other industries. The major challenge was to develop a non-faulty framework to detect meddling (to overcome the traditional ways). With the development of machine learning technology, detecting and stopping the meddling process in the early stages is much easier. In this study, the proposed framework uses numerous data collection and processing techniques and machine learning techniques to train the meddling data and detect anomalies. The proposed framework uses support vector machine (SVM) and K-nearest neighbor (KNN) machine learning algorithms to detect the meddling in a network entangled with blockchain technology to ensure the privacy and protection of models as well as communication data. SVM achieves the highest training detection accuracy (DA) and misclassification rate (MCR) of 99.59% and 0.41%, respectively, and SVM achieves the highest-testing DA and MCR of 99.05% and 0.95%, respectively. The presented framework portrays the best meddling detection results, which are very helpful for various communication and transaction processes.
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Affiliation(s)
- Muhammad Umar Nasir
- Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan
| | - Safiullah Khan
- Department of IT Convergence Engineering, Gachon University, Seongnam 13120, Korea
| | - Shahid Mehmood
- Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan
| | - Muhammad Adnan Khan
- Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, Korea
| | - Muhammad Zubair
- Faculty of Computing, Riphah International University, Islamabad Campus, Islamabad 45000, Pakistan
| | - Seong Oun Hwang
- Department of Computer Engineering, Gachon University, Seongnam 13120, Korea
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