Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy

Duran-Sierra, Elvis; Cheng, Shannon; Cuenca, Rodrigo; Ahmed, Beena; Ji, Jim; Yakovlev, Vladislav V.; Martinez, Mathias; Alkhalil, Moustafa; Al-Enazi, Hussain; Cheng, Yi-Shing Lisa; Wright, John M.; Busso, Carlos; Jo, Javier A.

doi:10.3390/cancers13194751

Cited by 27 publications

(36 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This analysis included 14 studies [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Table 1 presents the assessment of bias.…”

Section: Resultsmentioning

confidence: 99%

“… Forest plot of the diagnostic odds ratios for ( A ) screening only oral cancerous lesions [ 13 , 16 , 17 , 21 , 22 , 23 , 25 ] and ( B ) screening all premalignant mucosal lesions [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 ]. …”

Section: Figurementioning

confidence: 99%

“… Forest plots of ( A ) sensitivity, ( B ) specificity, and ( C ) negative predictive values for screening all premalignant mucosal lesions [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 ]. …”

Section: Figurementioning

confidence: 99%

“…The Egger's test result (p > 0.05) also shows that the possibility of publication bias is low. Forest plot of the diagnostic odds ratios for (A) screening only oral cancerous lesions [13,16,17,[21][22][23]25] and (B) screening all premalignant mucosal lesions [13][14][15][16][17][18][19][20][21]23,24].…”

Section: Diagnostic Accuracy Of Ai-assisted Screening Of Oral Mucosal...mentioning

confidence: 99%

See 3 more Smart Citations

Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

Kim

Hwang

2022

Cancers

View full text Add to dashboard Cite

The accuracy of artificial intelligence (AI)-assisted discrimination of oral cancerous lesions from normal mucosa based on mucosal images was evaluated. Two authors independently reviewed the database until June 2022. Oral mucosal disorder, as recorded by photographic images, autofluorescence, and optical coherence tomography (OCT), was compared with the reference results by histology findings. True-positive, true-negative, false-positive, and false-negative data were extracted. Seven studies were included for discriminating oral cancerous lesions from normal mucosa. The diagnostic odds ratio (DOR) of AI-assisted screening was 121.66 (95% confidence interval [CI], 29.60; 500.05). Twelve studies were included for discriminating all oral precancerous lesions from normal mucosa. The DOR of screening was 63.02 (95% CI, 40.32; 98.49). Subgroup analysis showed that OCT was more diagnostically accurate (324.33 vs. 66.81 and 27.63) and more negatively predictive (0.94 vs. 0.93 and 0.84) than photographic images and autofluorescence on the screening for all oral precancerous lesions from normal mucosa. Automated detection of oral cancerous lesions by AI would be a rapid, non-invasive diagnostic tool that could provide immediate results on the diagnostic work-up of oral cancer. This method has the potential to be used as a clinical tool for the early diagnosis of pathological lesions.

show abstract

“…This analysis included 14 studies [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Table 1 presents the assessment of bias.…”

Section: Resultsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Diagnostic Accuracy Of Ai-assisted Screening Of Oral Mucosal...mentioning

confidence: 99%

See 2 more Smart Citations

Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

Kim

Hwang

2022

Cancers

View full text Add to dashboard Cite

show abstract

“…We have recently explored pixel-level maFLIM features for the classification of oral dysplasia and early-stage cancer. 49 Pixel-level features, however, require the labeling of each pixel which is generally impractical. In this work, the maFLIM data were labeled at the lesion level based on the histopathology diagnosis obtained from the lesion biopsy samples; therefore, an image-level global feature extraction strategy was preferred.…”

Section: Discussionmentioning

confidence: 99%

Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

. Significance: Accurate early diagnosis of malignant skin lesions is critical in providing adequate and timely treatment; unfortunately, initial clinical evaluation of similar-looking benign and malignant skin lesions can result in missed diagnosis of malignant lesions and unnecessary biopsy of benign ones. Aim: To develop and validate a label-free and objective image-guided strategy for the clinical evaluation of suspicious pigmented skin lesions based on multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy. Approach: We tested the hypothesis that maFLIM-derived autofluorescence global features can be used in machine-learning (ML) models to discriminate malignant from benign pigmented skin lesions. Clinical widefield maFLIM dermoscopy imaging of 41 benign and 19 malignant pigmented skin lesions from 30 patients were acquired prior to tissue biopsy sampling. Three different pools of global image-level maFLIM features were extracted: multispectral intensity, time-domain biexponential, and frequency-domain phasor features. The classification potential of each feature pool to discriminate benign versus malignant pigmented skin lesions was evaluated by training quadratic discriminant analysis (QDA) classification models and applying a leave-one-patient-out cross-validation strategy. Results: Classification performance estimates obtained after unbiased feature selection were as follows: 68% sensitivity and 80% specificity with the phasor feature pool, 84% sensitivity, and 71% specificity with the biexponential feature pool, and 84% sensitivity and 32% specificity with the intensity feature pool. Ensemble combinations of QDA models trained with phasor and biexponential features yielded sensitivity of 84% and specificity of 90%, outperforming all other models considered. Conclusions: Simple classification ML models based on time-resolved (biexponential and phasor) autofluorescence global features extracted from maFLIM dermoscopy images have the potential to provide objective discrimination of malignant from benign pigmented lesions. ML-assisted maFLIM dermoscopy could potentially assist with the clinical evaluation of suspicious lesions and the identification of those patients benefiting the most from biopsy examination.

show abstract

An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research

Alajaji,

Khoury,

Jessri

et al. 2024

Head and Neck Pathol

View full text Add to dashboard Cite

Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy

Cited by 27 publications

References 46 publications

Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning

An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research

Contact Info

Product

Resources

About