Detecting Oral Cancer: The Potential of Artificial Intelligence

Vats, Rishabh; Rai, Ritu; Kumar, Manoj

doi:10.2174/1573405618666220408103549

Cited by 5 publications

(6 citation statements)

References 33 publications

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“…The proposed hybrid seek-based ensemble classifier was compared to other ensemble classifiers, including the AdaBoost classifier (EM1) [ 31 ], decision tree classifier (EM2) [ 6 , 32 ], random forest classifier (EM3) [ 9 , 33 ], K-nearest neighbor (EM4) classifier [ 34 ], support vector machine (EM5) classifier [ 13 , 35 ], deep learning classifier (EM6) [ 22 ], convolutional neural network (EM7) classifier [ 18 , 36 ], crow search optimization-based ensemble classifier [ 37 ] (EM8), and squirrel search optimization classifier (EM9) [ 38 ].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Most of the seizure prediction strategies are user-specific due to the variation in the type and location of the seizure with the EEG signals of patients. The conventional technique of seizure prediction consists of processes such as pre-processing of signals, selection of features, and classification [ 13 , 14 , 15 , 16 ]. The pre-processing step is executed to remove unwanted noise, enhance signal quality, and so on.…”

Section: Introductionmentioning

confidence: 99%

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Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

Kapoor

Nagpal

Jain

et al. 2022

Sensors

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Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique’s accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

Kapoor

Nagpal

Jain

et al. 2022

Sensors

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“…9,17 In the medical literature focusing on head and neck pathologies, there is some evidence of a great advance in the use of AI approaches for the diagnosis of OSCC. [18][19][20][21][22][23] 26 Additionally, only a few studies used radiomic data for intraosseous lesions detections, segmentation, or diagnosis 27 but none investigated approaches using histopathological slides for OT diagnosis. These tumors comprise a heterogeneous group of lesions ranging from hamartomatous to benign and malignant neoplasms.…”

Section: Cnn Trainingmentioning

confidence: 99%

Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas

Giraldo‐Roldan,

Ribeiro,

Araújo

et al. 2023

J Oral Pathology Medicine

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BackgroundOdontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Within this classification, ameloblastoma and ameloblastic carcinoma (AC) represent a diagnostic challenge in daily histopathological practice due to their similar characteristics and the limitations that incisional biopsies represent. From these premises, we wanted to test the usefulness of models based on artificial intelligence (AI) in the field of oral and maxillofacial pathology for differential diagnosis. The main advantages of integrating Machine Learning (ML) with microscopic and radiographic imaging is the ability to significantly reduce intra‐and inter observer variability and improve diagnostic objectivity and reproducibility.MethodsThirty Digitized slides were collected from different diagnostic centers of oral pathology in Brazil. After performing manual annotation in the region of interest, the images were segmented and fragmented into small patches. In the supervised learning methodology for image classification, three models (ResNet50, DenseNet, and VGG16) were focus of investigation to provide the probability of an image being classified as class0 (i.e., ameloblastoma) or class1 (i.e., Ameloblastic carcinoma).ResultsThe training and validation metrics did not show convergence, characterizing overfitting. However, the test results were satisfactory, with an average for ResNet50 of 0.75, 0.71, 0.84, 0.65, and 0.77 for accuracy, precision, sensitivity, specificity, and F1‐score, respectively.ConclusionsThe models demonstrated a strong potential of learning, but lack of generalization ability. The models learn fast, reaching a training accuracy of 98%. The evaluation process showed instability in validation; however, acceptable performance in the testing process, which may be due to the small data set. This first investigation opens an opportunity for expanding collaboration to incorporate more complementary data; as well as, developing and evaluating new alternative models.

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“…Common applications of AI in oral diagnosis and dentomaxillofacial radiology are as follows: Oral cancer prognosis and assessment of oral cancer risk [ 45 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]; Determination of temporomandibular joint disorder progression and temporomandibular internal derangements [ 27 , 30 , 34 , 38 , 63 ]; Interpretation of conventional 2D imaging [ 31 , 64 , 65 , 66 , 67 , 68 ]; Interpretation of cone beam computed tomography and other 3D imaging methods [ 1 , 10 , 12 , 17 , 18 , 19 , 21 , 23 , 27 , 69 , 70 , 71 ]. …”

Section: Introductionmentioning

confidence: 99%

“…Oral cancer prognosis and assessment of oral cancer risk [ 45 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ];…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Teeth, Crown–Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls

et al. 2023

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Background: The aim of our study is to provide successful automatic segmentation of various objects on orthopantomographs (OPGs). Methods: 8138 OPGs obtained from the archives of the Department of Dentomaxillofacial Radiology were included. OPGs were converted into PNGs and transferred to the segmentation tool’s database. All teeth, crown–bridge restorations, dental implants, composite–amalgam fillings, dental caries, residual roots, and root canal fillings were manually segmented by two experts with the manual drawing semantic segmentation technique. Results: The intra-class correlation coefficient (ICC) for both inter- and intra-observers for manual segmentation was excellent (ICC > 0.75). The intra-observer ICC was found to be 0.994, while the inter-observer reliability was 0.989. No significant difference was detected amongst observers (p = 0.947). The calculated DSC and accuracy values across all OPGs were 0.85 and 0.95 for the tooth segmentation, 0.88 and 0.99 for dental caries, 0.87 and 0.99 for dental restorations, 0.93 and 0.99 for crown–bridge restorations, 0.94 and 0.99 for dental implants, 0.78 and 0.99 for root canal fillings, and 0.78 and 0.99 for residual roots, respectively. Conclusions: Thanks to faster and automated diagnoses on 2D as well as 3D dental images, dentists will have higher diagnosis rates in a shorter time even without excluding cases.

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Detecting Oral Cancer: The Potential of Artificial Intelligence

Cited by 5 publications

References 33 publications

Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas

Automatic Segmentation of Teeth, Crown–Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls

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