Automatic discrimination of Yamamoto-Kohama classification by machine learning approach for invasive pattern of oral squamous cell carcinoma using digital microscopic images: a retrospective study

Yoshizawa, Kunio; Ando, Hideyuki; Kimura, Yujiro; Kawashiri, Shin‐ya; Yokomichi, Hiroshi; Moroi, Akinori; Ueki, Koichiro

doi:10.1016/j.oooo.2021.10.004

Cited by 11 publications

(7 citation statements)

References 58 publications

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“…AI technology has been mainly applied for diagnosing OSCC [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ], differentiating between normal and malignant conditions [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], forming an early diagnosis of OSCC [ 37 ], predicting the survival of patients [ 38 , 39 ], and grading the severity of OSCC [ 40 ]. In this systematic review, 17 studies were reported using convolutional neural networks (CNNs), while the other two were conducted utilizing capsule networks and a hybrid technique (CNN + ANN), as depicted in Table 2 [ 15 , 16 ].…”

Section: Resultsmentioning

confidence: 99%

“…All the studies had implemented a highly uniform training system, resulting in a low risk of bias for the index test in both arms of QUADAS-2. Six studies [ 22 , 23 , 25 , 28 , 38 , 39 ] had used human observations as the reference standard, and hence 30% of the studies reported a high risk in bias assessment and applicability concern. Overall, there was a low risk of bias in both arms across all categories of the included studies.…”

Section: Resultsmentioning

confidence: 99%

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Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review

Khanagar,

Alkadi,

Alghilan

et al. 2023

Biomedicines

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Oral cancer (OC) is one of the most common forms of head and neck cancer and continues to have the lowest survival rates worldwide, even with advancements in research and therapy. The prognosis of OC has not significantly improved in recent years, presenting a persistent challenge in the biomedical field. In the field of oncology, artificial intelligence (AI) has seen rapid development, with notable successes being reported in recent times. This systematic review aimed to critically appraise the available evidence regarding the utilization of AI in the diagnosis, classification, and prediction of oral cancer (OC) using histopathological images. An electronic search of several databases, including PubMed, Scopus, Embase, the Cochrane Library, Web of Science, Google Scholar, and the Saudi Digital Library, was conducted for articles published between January 2000 and January 2023. Nineteen articles that met the inclusion criteria were then subjected to critical analysis utilizing QUADAS-2, and the certainty of the evidence was assessed using the GRADE approach. AI models have been widely applied in diagnosing oral cancer, differentiating normal and malignant regions, predicting the survival of OC patients, and grading OC. The AI models used in these studies displayed an accuracy in a range from 89.47% to 100%, sensitivity from 97.76% to 99.26%, and specificity ranging from 92% to 99.42%. The models’ abilities to diagnose, classify, and predict the occurrence of OC outperform existing clinical approaches. This demonstrates the potential for AI to deliver a superior level of precision and accuracy, helping pathologists significantly improve their diagnostic outcomes and reduce the probability of errors. Considering these advantages, regulatory bodies and policymakers should expedite the process of approval and marketing of these products for application in clinical scenarios.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review

Khanagar,

Alkadi,

Alghilan

et al. 2023

Biomedicines

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“…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 18‐23 . Das et al proposed a two‐stage approach using CNN and a texture‐based random forest classifier to detect and segment keratin beads using texture‐based characteristics, with accuracy of 96.88% 24 .…”

Section: Discussionmentioning

confidence: 99%

“…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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“… AI systems analyze images obtained through intraoral cameras or other imaging devices to identify abnormalities or potential signs of oral cancer ( 14 ). AI algorithms are trained to recognize and classify oral lesions, including ulcers, white or red patches, and malignant lesions ( 14 , 15 ). AI does a good job in diagnosis but when it comes to surgical decisions, caution is warranted.…”

Section: Current Trends In Dental Education and Practicementioning

confidence: 99%

Dental education and practice: past, present, and future trends

Spielman

2024

Front. Oral. Health

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This position paper explores the historical transitions and current trends in dental education and practice and attempts to predict the future. Dental education and practice landscape, especially after the COVID-19 epidemic, are at a crossroads. Four fundamental forces are shaping the future: the escalating cost of education, the laicization of dental care, the corporatization of dental care, and technological advances. Dental education will likely include individualized, competency-based, asynchronous, hybrid, face-to-face, and virtual education with different start and end points for students. Dental practice, similarly, will be hybrid, with both face-to-face and virtual opportunities for patient care. Artificial intelligence will drive efficiencies in diagnosis, treatment, and office management.

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Automatic discrimination of Yamamoto-Kohama classification by machine learning approach for invasive pattern of oral squamous cell carcinoma using digital microscopic images: a retrospective study

Cited by 11 publications

References 58 publications

Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review

Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review

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

Dental education and practice: past, present, and future trends

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