Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Ren, Ruiyang; Luo, Han; Su, Chongying; Yao, Yang; Liao, Wen

doi:10.7717/peerj.11451

Cited by 41 publications

(32 citation statements)

References 152 publications

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“…Of the 7 included systematic reviews [7,[18][19][20][21][22][23], 4 were conducted in Asia (i.e., 2 each from China and India) [18,19,21,22] while 2 reviews was conducted in Europe (Finland) [7,23] and one review in the United States [20] (Table 2). The quality appraisal for all the review papers considered in this study indicated that 42.9% showed high quality [7,21,22] while 57.1% showed medium quality [18][19][20]23]. Three of the reviews were conducted in the year 2020 [18][19][20] while four were conducted in the year 2021 [7,[21][22][23].…”

Section: Characteristics Of Relevant Studiesmentioning

confidence: 99%

“…The processed training set is fed into the network through the convolutional layer. This layer automatically performs the image feature extraction to produce a feature map [21,22,[52][53][54][55]. However, the produced feature map is still large and needs to be downsized [21].…”

Section: Pipeline For Building a Deep Learning-based Prognostic Modelmentioning

confidence: 99%

“…This layer automatically performs the image feature extraction to produce a feature map [21,22,[52][53][54][55]. However, the produced feature map is still large and needs to be downsized [21]. An intuitive approach produced to downsize the feature map is the pooling process which works in a similar way as the earlier described convolution operation but with a different purpose (i.e., to downsize the feature map) [21,54].…”

Section: Pipeline For Building a Deep Learning-based Prognostic Modelmentioning

confidence: 99%

See 2 more Smart Citations

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Alabi¹,

Almangush²,

Elmusrati³

et al. 2022

Front. Oral. Health

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Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

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Section: Characteristics Of Relevant Studiesmentioning

confidence: 99%

Section: Pipeline For Building a Deep Learning-based Prognostic Modelmentioning

confidence: 99%

Section: Pipeline For Building a Deep Learning-based Prognostic Modelmentioning

confidence: 99%

See 1 more Smart Citation

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Alabi¹,

Almangush²,

Elmusrati³

et al. 2022

Front. Oral. Health

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show abstract

“…The retrieved feature information must now be fed into the modelling process. On the basis of the feedback from the results, fall-back processes such as hyperparameter adjustment and model adjustment are carried out [15]. The fundamental machine learning technique is shown in Fig.…”

Section: Learning In Image Processingmentioning

confidence: 99%

Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges

2021

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Artificial Intelligence (AI) has the ability to process huge datasets, disclose human essence computationally, and perform like humans as technology advances. Because of the necessity for precise diagnosis and improved patient care, AI technology has greatly influenced the healthcare industry. In the domains of dentistry and medicine, artificial intelligence has yet to come a long way. As a result, dentists must be aware of the potential implications for a profitable clinical practise in the future. In this paper, we present the current applications of AI in dentistry. The different types of AI techniques are introduced and summarized. The state-of-the-art literature is studied analysed. A comparative analysis on the different AI techniques in dentistry is presented. Further, the research challenges in the field of dentistry and future directions are also provided.

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“…AI applications are commonplace in digital everyday life, for example in the form of virtual assistants such as “Siri” or “Alexa” [ 1 , 4 ], and AI applications are implemented in various engineering fields [ 5 , 6 , 7 ]. In medicine, AI algorithms are ubiquitously used for image processing via feature extraction of specific images and target conducting [ 8 ]. For example, AI algorithms can be used for analyzation of chest X-ray and lung CT image samples images of COVID-19 patients and thus decrease the picture diagnostic time of the radiologist and accelerate clinical decisions [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

Bernauer

Zitzmann

Joda

2021

Sensors

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(1) Background: The rapid pace of digital development in everyday life is also reflected in dentistry, including the emergence of the first systems based on artificial intelligence (AI). This systematic review focused on the recent scientific literature and provides an overview of the application of AI in the dental discipline of prosthodontics. (2) Method: According to a modified PICO-strategy, an electronic (MEDLINE, EMBASE, CENTRAL) and manual search up to 30 June 2021 was carried out for the literature published in the last five years reporting the use of AI in the field of prosthodontics. (3) Results: 560 titles were screened, of which 30 abstracts and 16 full texts were selected for further review. Seven studies met the inclusion criteria and were analyzed. Most of the identified studies reported the training and application of an AI system (n = 6) or explored the function of an intrinsic AI system in a CAD software (n = 1). (4) Conclusions: While the number of included studies reporting the use of AI was relatively low, the summary of the obtained findings by the included studies represents the latest AI developments in prosthodontics demonstrating its application for automated diagnostics, as a predictive measure, and as a classification or identification tool. In the future, AI technologies will likely be used for collecting, processing, and organizing patient-related datasets to provide patient-centered, individualized dental treatment.

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Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Cited by 41 publications

References 152 publications

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges

The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

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