Evidence map›Paper›PMID 36979780›Full record

ArticleBiomedicines2023

Deep-Learning-Based Automated Identification and Visualization of Oral Cancer in Optical Coherence Tomography Images.

Zihan Yang, Hongming Pan, Jianwei Shang, Jun Zhang, Yanmei Liang

Open access · goldFull text read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
5.0field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 46 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Zihan YangInstitute of Modern Optics, Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Nankai University, Tianjin 300350, China.ORCID 0000-0003-1409-4725
Hongming PanInstitute of Modern Optics, Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Nankai University, Tianjin 300350, China.
Jianwei ShangDepartment of Oral Pathology, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin 300041, China.
Jun ZhangDepartment of Oral-Maxillofacial Surgery, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin 300041, China.
Yanmei LiangInstitute of Modern Optics, Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Nankai University, Tianjin 300350, China.ORCID 0000-0002-8846-391X
Nankai University · CNTianjin Stomatological Hospital · CN

Funding

Beijing-Tianjin-Hebei Basic Research Cooperation Special Program 19JCZDJC65300National Natural Science Foundation of China 61875092Science and Technology Support Program of Tianjin 17YFZCSY00740
6 · The paper itself

Abstract

Early detection and diagnosis of oral cancer are critical for a better prognosis, but accurate and automatic identification is difficult using the available technologies. Optical coherence tomography (OCT) can be used as diagnostic aid due to the advantages of high resolution and non-invasion. We aim to evaluate deep-learning-based algorithms for OCT images to assist clinicians in oral cancer screening and diagnosis. An OCT data set was first established, including normal mucosa, precancerous lesion, and oral squamous cell carcinoma. Then, three kinds of convolutional neural networks (CNNs) were trained and evaluated by using four metrics (accuracy, precision, sensitivity, and specificity). Moreover, the CNN-based methods were compared against machine learning approaches through the same dataset. The results show the performance of CNNs, with a classification accuracy of up to 96.76%, is better than the machine-learning-based method with an accuracy of 92.52%. Moreover, visualization of lesions in OCT images was performed and the rationality and interpretability of the model for distinguishing different oral tissues were evaluated. It is proved that the automatic identification algorithm of OCT images based on deep learning has the potential to provide decision support for the effective screening and diagnosis of oral cancer.

Indexed as

deep learningidentificationmachine learningoptical coherence tomographyoral cancer

Identifiers

PMID36979780
PMCPMC10044902
OpenAlexW4323361550

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read30
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.