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Dong F, Yan J, Zhang X, Zhang Y, Liu D, Pan X, Xue L, Liu Y. Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery. Heliyon 2024; 10:e35742. [PMID: 39170321 PMCID: PMC11336844 DOI: 10.1016/j.heliyon.2024.e35742] [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: 03/05/2024] [Revised: 07/27/2024] [Accepted: 08/02/2024] [Indexed: 08/23/2024] Open
Abstract
Application of deep learning (DL) and machine learning (ML) is rapidly increasing in the medical field. DL is gaining significance for medical image analysis, particularly, in oral and maxillofacial surgeries. Owing to the ability to accurately identify and categorize both diseased and normal soft- and hard-tissue structures, DL has high application potential in the diagnosis and treatment of tumors and in orthognathic surgeries. Moreover, DL and ML can be used to develop prediction models that can aid surgeons to assess prognosis by analyzing the patient's medical history, imaging data, and surgical records, develop more effective treatment strategies, select appropriate surgical modalities, and evaluate the risk of postoperative complications. Such prediction models can play a crucial role in the selection of treatment strategies for oral and maxillofacial surgeries. Their practical application can improve the utilization of medical staff, increase the treatment accuracy and efficiency, reduce surgical risks, and provide an enhanced treatment experience to patients. However, DL and ML face limitations, such as data drift, unstable model results, and vulnerable social trust. With the advancement of social concepts and technologies, the use of these models in oral and maxillofacial surgery is anticipated to become more comprehensive and extensive.
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Affiliation(s)
- Fanqiao Dong
- School of Stomatology, China Medical University, Shenyang, China
| | - Jingjing Yan
- Hospital of Stomatology, China Medical University, Shenyang, China
| | - Xiyue Zhang
- School of Stomatology, China Medical University, Shenyang, China
| | - Yikun Zhang
- School of Stomatology, China Medical University, Shenyang, China
| | - Di Liu
- School of Stomatology, China Medical University, Shenyang, China
| | - Xiyun Pan
- School of Stomatology, China Medical University, Shenyang, China
| | - Lei Xue
- School of Stomatology, China Medical University, Shenyang, China
- Hospital of Stomatology, China Medical University, Shenyang, China
| | - Yu Liu
- First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
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2
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Moharrami M, Azimian Zavareh P, Watson E, Singhal S, Johnson AEW, Hosni A, Quinonez C, Glogauer M. Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review. PLoS One 2024; 19:e0307531. [PMID: 39046953 PMCID: PMC11268644 DOI: 10.1371/journal.pone.0307531] [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: 04/30/2024] [Accepted: 07/07/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data. METHODS A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70-0.79 vs. 0.66-0.76, and for all sub-sites including oral cavity (0.73-0.89 vs. 0.69-0.77) and larynx (0.71-0.85 vs. 0.57-0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75-0.97, with an F1-score of 0.65-0.89 for HNC; AUROC of 0.61-0.91 and F1-score of 0.58-0.86 for the oral cavity; and AUROC of 0.76-0.97 and F1-score of 0.63-0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89. CONCLUSIONS ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets.
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Affiliation(s)
- Mohammad Moharrami
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
| | - Parnia Azimian Zavareh
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
| | - Erin Watson
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - Sonica Singhal
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Chronic Disease and Injury Prevention Department, Health Promotion, Public Health Ontario, Toronto, Canada
| | - Alistair E. W. Johnson
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - Ali Hosni
- Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Canada
| | - Carlos Quinonez
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Schulich School of Medicine & Dentistry, Western University, London, Canada
| | - Michael Glogauer
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada
- Department of Dentistry, Centre for Advanced Dental Research and Care, Mount Sinai Hospital, Toronto, Canada
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Uppal S, Kumar Shrivastava P, Khan A, Sharma A, Kumar Shrivastav A. Machine learning methods in predicting the risk of malignant transformation of oral potentially malignant disorders: A systematic review. Int J Med Inform 2024; 186:105421. [PMID: 38552265 DOI: 10.1016/j.ijmedinf.2024.105421] [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: 11/28/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active intervention in high-risk patients and routine follow-up in low-risk ones. Machine learning models has shown tremendous potential in several areas of dentistry that strongly suggest its application to estimate rate of malignant transformation of precancerous lesions. METHODS A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist. RESULTS Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%. CONCLUSION Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.
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Affiliation(s)
- Simran Uppal
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | | | - Atiya Khan
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Aditi Sharma
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Ayush Kumar Shrivastav
- Computer Science and Engineering, Centre for Development of Advanced Computing, Noida, Uttar Pradesh, India.
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Hassan J, Saeed SM, Deka L, Uddin MJ, Das DB. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics 2024; 16:260. [PMID: 38399314 PMCID: PMC10892549 DOI: 10.3390/pharmaceutics16020260] [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: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
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Affiliation(s)
- Jasmin Hassan
- Drug Delivery & Therapeutics Lab, Dhaka 1212, Bangladesh; (J.H.); (S.M.S.)
| | | | - Lipika Deka
- Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK;
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Diganta B. Das
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK
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Kurt-Bayrakdar S, Bayrakdar İŞ, Yavuz MB, Sali N, Çelik Ö, Köse O, Uzun Saylan BC, Kuleli B, Jagtap R, Orhan K. Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study. BMC Oral Health 2024; 24:155. [PMID: 38297288 PMCID: PMC10832206 DOI: 10.1186/s12903-024-03896-5] [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: 11/09/2023] [Accepted: 01/15/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns. METHODS A total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis. RESULTS The system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively). CONCLUSIONS AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.
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Affiliation(s)
- Sevda Kurt-Bayrakdar
- Faculty of Dentistry, Department of Periodontology, Eskisehir Osmangazi University, Eskisehir, 26240, Turkey.
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA.
| | - İbrahim Şevki Bayrakdar
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Muhammet Burak Yavuz
- Faculty of Dentistry, Department of Periodontology, Eskisehir Osmangazi University, Eskisehir, 26240, Turkey
| | - Nichal Sali
- Faculty of Dentistry, Department of Periodontology, Eskisehir Osmangazi University, Eskisehir, 26240, Turkey
| | - Özer Çelik
- Faculty of Science, Department of Mathematics and Computer Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Oğuz Köse
- Faculty of Dentistry, Department of Periodontology, Recep Tayyip Erdogan University, Rize, Turkey
| | | | - Batuhan Kuleli
- Faculty of Dentistry, Department of Orthodontics, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Rohan Jagtap
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA
| | - Kaan Orhan
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Ankara University, Ankara, Turkey
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Rokhshad R, Mohammad-Rahimi H, Price JB, Shoorgashti R, Abbasiparashkouh Z, Esmaeili M, Sarfaraz B, Rokhshad A, Motamedian SR, Soltani P, Schwendicke F. Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis. Clin Oral Investig 2024; 28:88. [PMID: 38217733 DOI: 10.1007/s00784-023-05475-4] [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: 01/25/2023] [Accepted: 12/21/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVE This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs. MATERIALS AND METHOD Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation. RESULTS After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221). CONCLUSIONS AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far. CLINICAL RELEVANCE Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran
| | - Jeffery B Price
- Department of Oncology and Diagnostic Sciences, University of Maryland, School of Dentistry, Baltimore, Maryland 650 W Baltimore St, Baltimore, MD, 21201, USA
| | - Reyhaneh Shoorgashti
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | | | - Mahdieh Esmaeili
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | - Bita Sarfaraz
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran
| | - Arad Rokhshad
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | - Saeed Reza Motamedian
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran.
| | - Parisa Soltani
- Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Salamat Blv, Isfahan Dental School, Isfahan, Iran
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Nepales, Italy
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Charitépl. 1, 10117, Berlin, Germany
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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Nagarajan B, Chakravarthy S, Venkatesan VK, Ramakrishna MT, Khan SB, Basheer S, Albalawi E. A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma. Diagnostics (Basel) 2023; 13:3461. [PMID: 37998597 PMCID: PMC10670914 DOI: 10.3390/diagnostics13223461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/25/2023] Open
Abstract
One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient's histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer.
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Affiliation(s)
- Bharanidharan Nagarajan
- School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, India; (B.N.); (V.K.V.)
| | - Sannasi Chakravarthy
- Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam 638401, India;
| | - Vinoth Kumar Venkatesan
- School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, India; (B.N.); (V.K.V.)
| | - Mahesh Thyluru Ramakrishna
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-Be University), Bangalore 562112, India
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester M5 4WT, UK
- Department of Engineering and Environment, University of Religions and Denominations, Qom 13357, Iran
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Eid Albalawi
- Department of Computer Science, School of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
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Ahmad M, Irfan MA, Sadique U, Haq IU, Jan A, Khattak MI, Ghadi YY, Aljuaid H. Multi-Method Analysis of Histopathological Image for Early Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning and Hybrid Techniques. Cancers (Basel) 2023; 15:5247. [PMID: 37958422 PMCID: PMC10650156 DOI: 10.3390/cancers15215247] [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: 09/28/2023] [Revised: 10/22/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient's chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.
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Affiliation(s)
- Mehran Ahmad
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; (M.A.); (A.J.); (M.I.K.)
- AIH, Intelligent Information Processing Lab (NCAI), University of Engineering and Technology, Peshawar 25000, Pakistan; (U.S.); (I.u.H.)
| | - Muhammad Abeer Irfan
- Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan;
| | - Umar Sadique
- AIH, Intelligent Information Processing Lab (NCAI), University of Engineering and Technology, Peshawar 25000, Pakistan; (U.S.); (I.u.H.)
- Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan;
| | - Ihtisham ul Haq
- AIH, Intelligent Information Processing Lab (NCAI), University of Engineering and Technology, Peshawar 25000, Pakistan; (U.S.); (I.u.H.)
- Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
| | - Atif Jan
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; (M.A.); (A.J.); (M.I.K.)
| | - Muhammad Irfan Khattak
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; (M.A.); (A.J.); (M.I.K.)
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Al Ain 15551, United Arab Emirates;
| | - Hanan Aljuaid
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), Riyadh 11671, Saudi Arabia
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Badawy M, Balaha HM, Maklad AS, Almars AM, Elhosseini MA. Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs. Biomimetics (Basel) 2023; 8:499. [PMID: 37887629 PMCID: PMC10604828 DOI: 10.3390/biomimetics8060499] [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: 08/18/2023] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with 'ImageNet' weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a 'normal' class with 2494 images and an 'OSCC' (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis.
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Affiliation(s)
- Mahmoud Badawy
- Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah 41461, Saudi Arabia
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
| | - Hossam Magdy Balaha
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
- Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY 40208, USA
| | - Ahmed S. Maklad
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suif 62521, Egypt
| | - Abdulqader M. Almars
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
| | - Mostafa A. Elhosseini
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
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11
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Huqh MZU, Abdullah JY, AL-Rawas M, Husein A, Ahmad WMAW, Jamayet NB, Genisa M, Yahya MRB. Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate. Diagnostics (Basel) 2023; 13:3025. [PMID: 37835768 PMCID: PMC10572375 DOI: 10.3390/diagnostics13193025] [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: 08/21/2023] [Revised: 09/14/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
INTRODUCTION Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals. METHODS This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen. RESULTS The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study's output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%. CONCLUSION The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.
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Affiliation(s)
- Mohamed Zahoor Ul Huqh
- Orthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia;
| | - Johari Yap Abdullah
- Craniofacial Imaging Lab, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
| | - Matheel AL-Rawas
- Prosthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia;
| | - Adam Husein
- Prosthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia;
| | - Wan Muhamad Amir W Ahmad
- Department of Biostatistics, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia;
| | - Nafij Bin Jamayet
- Division of Restorative Dentistry (Prosthodontics), School of Dentistry, International Medical University, Bukit Jalil, Kuala Lumpur 57000, Malaysia;
| | - Maya Genisa
- Biomedical Programme, Faculty of Pascasarjana, YARSI University, Jakarta 10510, Indonesia;
| | - Mohd Rosli Bin Yahya
- Oral & Maxillofacial Department, Hospital Raja Perempuan Zainab II, Kota Bharu 15586, Malaysia;
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Sivari E, Senirkentli GB, Bostanci E, Guzel MS, Acici K, Asuroglu T. Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review. Diagnostics (Basel) 2023; 13:2512. [PMID: 37568875 PMCID: PMC10416832 DOI: 10.3390/diagnostics13152512] [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: 07/11/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019-May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.
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Affiliation(s)
- Esra Sivari
- Department of Computer Engineering, Cankiri Karatekin University, Cankiri 18100, Turkey
| | | | - Erkan Bostanci
- Department of Computer Engineering, Ankara University, Ankara 06830, Turkey
| | | | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara 06830, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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13
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Gomes RFT, Schuch LF, Martins MD, Honório EF, de Figueiredo RM, Schmith J, Machado GN, Carrard VC. Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine-A Systematic Review. J Digit Imaging 2023; 36:1060-1070. [PMID: 36650299 PMCID: PMC10287602 DOI: 10.1007/s10278-023-00775-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/19/2023] Open
Abstract
Artificial neural networks (ANN) are artificial intelligence (AI) techniques used in the automated recognition and classification of pathological changes from clinical images in areas such as ophthalmology, dermatology, and oral medicine. The combination of enterprise imaging and AI is gaining notoriety for its potential benefits in healthcare areas such as cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, and endoscopic. The present study aimed to analyze, through a systematic literature review, the application of performance of ANN and deep learning in the recognition and automated classification of lesions from clinical images, when comparing to the human performance. The PRISMA 2020 approach (Preferred Reporting Items for Systematic Reviews and Meta-analyses) was used by searching four databases of studies that reference the use of IA to define the diagnosis of lesions in ophthalmology, dermatology, and oral medicine areas. A quantitative and qualitative analyses of the articles that met the inclusion criteria were performed. The search yielded the inclusion of 60 studies. It was found that the interest in the topic has increased, especially in the last 3 years. We observed that the performance of IA models is promising, with high accuracy, sensitivity, and specificity, most of them had outcomes equivalent to human comparators. The reproducibility of the performance of models in real-life practice has been reported as a critical point. Study designs and results have been progressively improved. IA resources have the potential to contribute to several areas of health. In the coming years, it is likely to be incorporated into everyday life, contributing to the precision and reducing the time required by the diagnostic process.
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Affiliation(s)
- Rita Fabiane Teixeira Gomes
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil.
| | - Lauren Frenzel Schuch
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | - Manoela Domingues Martins
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | | | - Rodrigo Marques de Figueiredo
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Jean Schmith
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Giovanna Nunes Machado
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Vinicius Coelho Carrard
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil
- Department of Epidemiology, School of Medicine, TelessaúdeRS-UFRGS, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
- Department of Oral Medicine, Otorhinolaryngology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
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14
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Dixit S, Kumar A, Srinivasan K. A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions. Diagnostics (Basel) 2023; 13:diagnostics13071353. [PMID: 37046571 PMCID: PMC10093759 DOI: 10.3390/diagnostics13071353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 03/25/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people’s lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI’s drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.
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Affiliation(s)
- Shriniket Dixit
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Anant Kumar
- School of Bioscience and Technology, Vellore Institute of Technology, Vellore 632014, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
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de Souza LL, Fonseca FP, Araújo ALD, Lopes MA, Vargas PA, Khurram SA, Kowalski LP, Dos Santos HT, Warnakulasuriya S, Dolezal J, Pearson AT, Santos-Silva AR. Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review. J Oral Pathol Med 2023; 52:197-205. [PMID: 36792771 DOI: 10.1111/jop.13414] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 02/17/2023]
Abstract
Oral potentially malignant disorders represent precursor lesions that may undergo malignant transformation to oral cancer. There are many known risk factors associated with the development of oral potentially malignant disorders, and contribute to the risk of malignant transformation. Although many advances have been reported to understand the biological behavior of oral potentially malignant disorders, their clinical features that indicate the characteristics of malignant transformation are not well established. Early diagnosis of malignancy is the most important factor to improve patients' prognosis. The integration of machine learning into routine diagnosis has recently emerged as an adjunct to aid clinical examination. Increased performances of artificial intelligence AI-assisted medical devices are claimed to exceed the human capability in the clinical detection of early cancer. Therefore, the aim of this narrative review is to introduce artificial intelligence terminology, concepts, and models currently used in oncology to familiarize oral medicine scientists with the language skills, best research practices, and knowledge for developing machine learning models applied to the clinical detection of oral potentially malignant disorders.
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Affiliation(s)
- Lucas Lacerda de Souza
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Felipe Paiva Fonseca
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
- Department of Oral Surgery and Pathology, School of Dentistry, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Marcio Ajudarte Lopes
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Pablo Agustin Vargas
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Syed Ali Khurram
- Unit of Oral & Maxillofacial Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery, University of Sao Paulo Medical School and Department of Head and Neck Surgery and Otorhinolaryngology, AC Camargo Cancer Center, Sao Paulo, Brazil
| | - Harim Tavares Dos Santos
- Department of Otolaryngology-Head and Neck Surgery, University of Missouri, Columbia, Missouri, USA
- Department of Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Saman Warnakulasuriya
- King's College London, London, UK
- WHO Collaborating Centre for Oral Cancer, London, UK
| | - James Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Alexander T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Alan Roger Santos-Silva
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
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Premachandran S, Haldavnekar R, Das S, Venkatakrishnan K, Tan B. DEEP Surveillance of Brain Cancer Using Self-Functionalized 3D Nanoprobes for Noninvasive Liquid Biopsy. ACS NANO 2022; 16:17948-17964. [PMID: 36112671 DOI: 10.1021/acsnano.2c04187] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Brain cancers, one of the most fatal malignancies, require accurate diagnosis for guided therapeutic intervention. However, conventional methods for brain cancer prognosis (imaging and tissue biopsy) face challenges due to the complex nature and inaccessible anatomy of the brain. Therefore, deep analysis of brain cancer is necessary to (i) detect the presence of a malignant tumor, (ii) identify primary or secondary origin, and (iii) find where the tumor is housed. In order to provide a diagnostic technique with such exhaustive information here, we attempted a liquid biopsy-based deep surveillance of brain cancer using a very minimal amount of blood serum (5 μL) in real time. We hypothesize that holistic analysis of serum can act as a reliable source for deep brain cancer surveillance. To identify minute amounts of tumor-derived material in circulation, we synthesized an ultrasensitive 3D nanosensor, adopted SERS as a diagnostic methodology, and undertook a DEEP neural network-based brain cancer surveillance. Detection of primary and secondary tumor achieved 100% accuracy. Prediction of intracranial tumor location achieved 96% accuracy. This modality of using patient sera for deep surveillance is a promising noninvasive liquid biopsy tool with the potential to complement current brain cancer diagnostic methodologies.
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Affiliation(s)
- Srilakshmi Premachandran
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano Characterization Laboratory, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
| | - Rupa Haldavnekar
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano Characterization Laboratory, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
| | - Sunit Das
- Scientist, St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Institute of Medical Sciences, Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Krishnan Venkatakrishnan
- Keenan Research Center for Biomedical Science, Unity Health Toronto, Toronto, Ontario M5B 1W8, Canada
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
| | - Bo Tan
- Keenan Research Center for Biomedical Science, Unity Health Toronto, Toronto, Ontario M5B 1W8, Canada
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Nano Characterization Laboratory, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
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Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6364102. [PMID: 36210968 PMCID: PMC9546660 DOI: 10.1155/2022/6364102] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/04/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022]
Abstract
Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.
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Lyu PF, Wang Y, Meng QX, Fan PM, Ma K, Xiao S, Cao XC, Lin GX, Dong SY. Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis. Front Oncol 2022; 12:955668. [PMID: 36212413 PMCID: PMC9535738 DOI: 10.3389/fonc.2022.955668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 08/25/2022] [Indexed: 11/23/2022] Open
Abstract
Background Artificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC. Methods Scientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords. Results The total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified. Conclusion AAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.
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Affiliation(s)
- Peng-fei Lyu
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Yu Wang
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Qing-Xiang Meng
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Ping-ming Fan
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Ke Ma
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Sha Xiao
- International School of Public Health and One Health, Heinz Mehlhorn Academician Workstation, Hainan Medical University, Haikou, China
| | - Xun-chen Cao
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Guang-Xun Lin
- Department of Orthopedics, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- *Correspondence: Guang-Xun Lin, ; Si-yuan Dong,
| | - Si-yuan Dong
- Thoracic Department, The First Hospital of China Medical University, Shenyang, China
- *Correspondence: Guang-Xun Lin, ; Si-yuan Dong,
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Bansal K, Bathla RK, Kumar Y. Deep transfer learning techniques with hybrid optimization in early prediction and diagnosis of different types of oral cancer. Soft comput 2022. [DOI: 10.1007/s00500-022-07246-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Hegde S, Ajila V, Zhu W, Zeng C. Review of the Use of Artificial Intelligence in Early Diagnosis and Prevention of Oral Cancer. Asia Pac J Oncol Nurs 2022; 9:100133. [PMID: 36389623 PMCID: PMC9664349 DOI: 10.1016/j.apjon.2022.100133] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/12/2022] [Indexed: 11/30/2022] Open
Abstract
The global occurrence of oral cancer (OC) has increased in recent years. OC that is diagnosed in its advanced stages results in morbidity and mortality. The use of technology may be beneficial for early detection and diagnosis and thus help the clinician with better patient management. The advent of artificial intelligence (AI) has the potential to improve OC screening. AI can precisely analyze an enormous dataset from various imaging modalities and provide assistance in the field of oncology. This review focused on the applications of AI in the early diagnosis and prevention of OC. A literature search was conducted in the PubMed and Scopus databases using the search terminology “oral cancer” and “artificial intelligence.” Further information regarding the topic was collected by scrutinizing the reference lists of selected articles. Based on the information obtained, this article reviews and discusses the applications and advantages of AI in OC screening, early diagnosis, disease prediction, treatment planning, and prognosis. Limitations and the future scope of AI in OC research are also highlighted.
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21
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Can Artificial Intelligence (AI) assist in the diagnosis of oral mucosal lesions and/or oral cancer? Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 134:413-414. [PMID: 36054978 DOI: 10.1016/j.oooo.2022.07.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 11/23/2022]
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22
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Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning. SENSORS 2022; 22:s22103833. [PMID: 35632242 PMCID: PMC9146317 DOI: 10.3390/s22103833] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/06/2022] [Accepted: 05/17/2022] [Indexed: 02/06/2023]
Abstract
Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.
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Tang J, Wang J, Pan X. A Web-Based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: A Population-Based Study. Front Public Health 2022; 9:812395. [PMID: 35087789 PMCID: PMC8787310 DOI: 10.3389/fpubh.2021.812395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/13/2021] [Indexed: 01/26/2023] Open
Abstract
Background: Malignant bone tumors (MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival (OS) of elderly patients with MBT. Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomogram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model was. Decision curve analysis (DCA) was used to assess the clinical potential application value of the nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. The Kaplan-Meier (K-M) curve was used to test the difference in survival between the two patients. Results: A total of 1,641 patients were included, and they were randomly assigned to the training set (N = 1,156) and the validation set (N = 485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779 [0.759–0.799] and 0.801 [0.772–0.830], respectively. The AUC of the training and validation sets also showed similar results. The calibration curves of the training and validation sets indicated that the observed and predicted values were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging. Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.
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Affiliation(s)
- Jie Tang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenyang Medical College, Shenyang, China
| | - JinKui Wang
- Department of Orthopedics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Xiudan Pan
- Department of Biostatistics and Epidemiology, School of Public Health, Shenyang Medical College, Shenyang, China
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Patil S, Albogami S, Hosmani J, Mujoo S, Kamil MA, Mansour MA, Abdul HN, Bhandi S, Ahmed SSSJ. Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls. Diagnostics (Basel) 2022; 12:diagnostics12051029. [PMID: 35626185 PMCID: PMC9139975 DOI: 10.3390/diagnostics12051029] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/12/2022] [Accepted: 04/18/2022] [Indexed: 12/19/2022] Open
Abstract
Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry.
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Affiliation(s)
- Shankargouda Patil
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence:
| | - Sarah Albogami
- Department of Biotechnology, College of Science, Taif University, Taif 21944, Saudi Arabia;
| | - Jagadish Hosmani
- Department of Diagnostic Dental Sciences, Oral Pathology Division, Faculty of Dentistry, College of Dentistry, King Khalid University, Abha 61411, Saudi Arabia;
| | - Sheetal Mujoo
- Division of Oral Medicine & Radiology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Mona Awad Kamil
- Department of Preventive Dental Science, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Manawar Ahmad Mansour
- Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (M.A.M.); (H.N.A.)
| | - Hina Naim Abdul
- Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (M.A.M.); (H.N.A.)
| | - Shilpa Bhandi
- Department of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Shiek S. S. J. Ahmed
- Multi-Omics and Drug Discovery Lab, Chettinad Academy of Research and Education, Chennai 600130, India;
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Vats R, Rai R, Kumar M. Detecting Oral Cancer: The Potential of Artificial Intelligence. Curr Med Imaging 2022; 18:919-923. [PMID: 35400347 DOI: 10.2174/1573405618666220408103549] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/05/2022] [Accepted: 01/31/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Physical inspection is a simple way to diagnose oral cancer. Most cases of oral cancer, on the contrary, are diagnosed late, resulting in needless mortality and morbidity. While screening high-risk populations appear to be helpful, these people are often found in areas with minimal access to health care. In this paper, we have reviewed several aspects related to oral cancer such as its cause, the risk factors associated with it, India's oral cancer situation at the moment, various screening methods, and the ability of artificial intelligence in the detection and classification purpose. Oral cancer results can be enhanced by combining imaging and artificial intelligence approaches for better detection and diagnosis. OBJECTIVE This paper aims to cover the various oral cancer screening detection techniques that use Artificial Intelligence (AI). METHODS In this paper, we have covered the imaging methods that are used in screening oral cancer and after that the potential of AI for the detection of oral cancer. CONCLUSION This paper covers some of the main concepts regarding oral cancer and various AI methods used to detect it.
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Affiliation(s)
- Rishabh Vats
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Ritu Rai
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Manoj Kumar
- Department of Computer Engineering and Applications, GLA University, Mathura, India
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26
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Nath S, Raveendran R, Perumbure S. Artificial Intelligence and Its Application in the Early Detection of Oral Cancers. CLINICAL CANCER INVESTIGATION JOURNAL 2022. [DOI: 10.51847/h7wa0uhoif] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Yan KX, Liu L, Li H. Application of machine learning in oral and maxillofacial surgery. Artif Intell Med Imaging 2021; 2:104-114. [DOI: 10.35711/aimi.v2.i6.104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 02/06/2023] Open
Abstract
Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.
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Affiliation(s)
- Kai-Xin Yan
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lei Liu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hui Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
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Carrillo-Perez F, Pecho OE, Morales JC, Paravina RD, Della Bona A, Ghinea R, Pulgar R, Pérez MDM, Herrera LJ. Applications of artificial intelligence in dentistry: A comprehensive review. J ESTHET RESTOR DENT 2021; 34:259-280. [PMID: 34842324 DOI: 10.1111/jerd.12844] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/30/2021] [Accepted: 11/09/2021] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. MATERIALS AND METHODS The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. RESULTS Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. CONCLUSIONS The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. CLINICAL SIGNIFICANCE The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.
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Affiliation(s)
- Francisco Carrillo-Perez
- Department of Computer Architecture and Technology, E.T.S.I.I.T.-C.I.T.I.C. University of Granada, Granada, Spain
| | - Oscar E Pecho
- Post-Graduate Program in Dentistry, Dental School, University of Passo Fundo, Passo Fundo, Brazil
| | - Juan Carlos Morales
- Department of Computer Architecture and Technology, E.T.S.I.I.T.-C.I.T.I.C. University of Granada, Granada, Spain
| | - Rade D Paravina
- Department of Restorative Dentistry and Prosthodontics, School of Dentistry, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Alvaro Della Bona
- Post-Graduate Program in Dentistry, Dental School, University of Passo Fundo, Passo Fundo, Brazil
| | - Razvan Ghinea
- Department of Optics, Faculty of Science, University of Granada, Granada, Spain
| | - Rosa Pulgar
- Department of Stomatology, Campus Cartuja, University of Granada, Granada, Spain
| | - María Del Mar Pérez
- Department of Optics, Faculty of Science, University of Granada, Granada, Spain
| | - Luis Javier Herrera
- Department of Computer Architecture and Technology, E.T.S.I.I.T.-C.I.T.I.C. University of Granada, Granada, Spain
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Wu X, Yao Y, Dai Y, Diao P, Zhang Y, Zhang P, Li S, Jiang H, Cheng J. Identification of diagnostic and prognostic signatures derived from preoperative blood parameters for oral squamous cell carcinoma. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1220. [PMID: 34532357 PMCID: PMC8421978 DOI: 10.21037/atm-21-631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/17/2021] [Indexed: 11/06/2022]
Abstract
Background We aimed to develop novel diagnostic and prognostic signatures based on preoperative inflammatory, immunological, and nutritional parameters in blood (PIINPBs) by machine learning algorithms for patients with oral squamous cell carcinoma (OSCC). Methods A total of 486 OSCC patients and 200 age and gender-matched non-OSCC patients who were diagnosed and treated at our institution for noninfectious, nontumor diseases were retrospectively enrolled and divided into training and validation cohorts. Based on PIINPB, 6 machine learning classifiers including random forest, support vector machine, extreme gradient boosting, naive Bayes, neural network, and logistic regression were used to derive diagnostic models, while least absolute shrinkage and selection operator (LASSO) analyses were employed to construct prognostic signatures. A novel prognostic nomogram integrating a PIINPB-derived prognostic signature and selected clinicopathological parameters was further developed. Performances of these signatures were assessed by receiver operating characteristic (ROC) curves, calibrating curves, and decision tree. Results Diagnostic models developed by machine learning algorithms from 13 PIINPBs, which included counts of white blood cells (WBC), neutrophils (N), monocytes (M), lymphocytes (L), platelets (P), albumin (ALB), and hemoglobin (Hb), along with albumin-globulin ratio (A/G), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), displayed satisfactory discriminating capabilities in patients with or without OSCC, and among OSCC patients with diverse pathological grades and clinical stages. A prognostic signature based on 6 survival-associated PIINPBs (L, P, PNI, LMR, SII, A/G) served as an independent factor to predict patient survival. Moreover, a novel nomogram integrating prognostic signature and tumor size, pathological grade, cervical node metastasis, and clinical stage significantly enhanced prognostic power [3-year area under the curve (AUC) =0.825; 5-year AUC =0.845]. Conclusions Our results generated novel and robust diagnostic and prognostic signatures derived from PIINPBs by machine learning for OSCC. Performance of these signatures suggest the potential for PIINPBs to supplement current regimens and provide better patient stratification and prognostic prediction.
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Affiliation(s)
- Xiang Wu
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China.,Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, China
| | - Yuan Yao
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China
| | - Yibin Dai
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China
| | - Pengfei Diao
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China
| | - Yuchao Zhang
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China
| | - Ping Zhang
- Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, China
| | - Sheng Li
- Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, China
| | - Hongbing Jiang
- Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, China
| | - Jie Cheng
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China.,Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, China
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Khanagar SB, Naik S, Al Kheraif AA, Vishwanathaiah S, Maganur PC, Alhazmi Y, Mushtaq S, Sarode SC, Sarode GS, Zanza A, Testarelli L, Patil S. Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review. Diagnostics (Basel) 2021; 11:diagnostics11061004. [PMID: 34072804 PMCID: PMC8227647 DOI: 10.3390/diagnostics11061004] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 05/25/2021] [Accepted: 05/29/2021] [Indexed: 12/20/2022] Open
Abstract
Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression.
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Affiliation(s)
- Sanjeev B. Khanagar
- Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia;
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Sachin Naik
- Dental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia; (S.N.); (A.A.A.K.)
| | - Abdulaziz Abdullah Al Kheraif
- Dental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia; (S.N.); (A.A.A.K.)
| | - Satish Vishwanathaiah
- Department of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (S.V.); (P.C.M.)
| | - Prabhadevi C. Maganur
- Department of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (S.V.); (P.C.M.)
| | - Yaser Alhazmi
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Shazia Mushtaq
- College of Applied Medical Sciences, Dental Health Department, King Saud University, Riyadh 12372, Saudi Arabia;
| | - Sachin C. Sarode
- Department of Oral and Maxillofacial Pathology, Dr. D.Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune 411018, India; (S.C.S.); (G.S.S.)
| | - Gargi S. Sarode
- Department of Oral and Maxillofacial Pathology, Dr. D.Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune 411018, India; (S.C.S.); (G.S.S.)
| | - Alessio Zanza
- Department of Maxillo and Oro-Facial Sciences, University of Rome La Sapienza, 00185 Rome, Italy; (A.Z.); (L.T.)
| | - Luca Testarelli
- Department of Maxillo and Oro-Facial Sciences, University of Rome La Sapienza, 00185 Rome, Italy; (A.Z.); (L.T.)
| | - Shankargouda Patil
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
- Correspondence:
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