Development of a Multimodal Colposcopy for Characterization of Cervical Intraepithelial Neoplasia

Ren, Wenqi; Qu, Yingjie; Pei, Jiaojiao; Xiao, Linlin; Chang, Shufang; Xu, Ronald X.

doi:10.1115/1.4036335

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…A smartphone-based endoscope is based on off-the-shelf optics to magnify the images while reducing the signal-to-noise ratio by avoiding high digital zoom. A simple modification of optical components can enable additional functional imaging such as fluorescence, polarization, and multi-spectral imaging, and thus can further improve the visualization of the abnormal tissue [24,[40][41][42]. Furthermore, manipulation of the endoscopic device inside of the vagina is very intuitive and easy compared to that of conventional approaches that are manipulated outside of the vagina.…”

Section: Principal Resultsmentioning

confidence: 99%

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Bae

Roh

You

et al. 2020

JMIR Mhealth Uhealth

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Background Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing–based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations. Objective In this study, we demonstrate a new quantitative CC screening technique and implement a machine learning algorithm for smartphone-based endoscopic VIA. We also evaluated the diagnostic performance and practicability of the approach based on the results compared to the gold standard and from physicians’ interpretation. Methods A smartphone-based endoscope system was developed and applied to the VIA screening. A total of 20 patients were recruited for this study to evaluate the system. Overall, five were healthy, and 15 were patients who had shown a low to high grade of cervical intraepithelial neoplasia (CIN) from both colposcopy and cytology tests. Endoscopic VIA images were obtained before a loop electrosurgical excision procedure for patients with abnormal tissues, and their histology tissues were collected. Endoscopic VIA images were assessed by four expert physicians relative to the gold standard of histopathology. Also, VIA features were extracted from multiple steps of image processing techniques to find the differences between abnormal (CIN2+) and normal (≤CIN1). By using the extracted features, the performance of different machine learning classifiers, such as k-nearest neighbors (KNN), support vector machine, and decision tree (DT), were compared to find the best algorithm for VIA. After determining the best performing classifying model, it was used to evaluate the screening performance of VIA. Results An average accuracy of 78%, with a Cohen kappa of 0.571, was observed for the evaluation of the system by four physicians. Through image processing, 240 sliced images were obtained from the cervicogram at each clock position, and five features of VIA were extracted. Among the three models, KNN showed the best performance for finding VIA within holdout 10-fold cross-validation, with an accuracy of 78.3%, area under the curve of 0.807, a specificity of 80.3%, and a sensitivity of 75.0%, respectively. The trained model performed using an unprovided data set resulted in an accuracy of 80.8%, specificity of 84.1%, and sensitivity of 71.9%. Predictions were visualized with intuitive color labels, indicating the normal/abnormal tissue using a circular clock-type segmentation. Calculating the overlapped abnormal tissues between the gold standard and predicted value, the KNN model overperformed the average assessments of physicians for finding VIA. Conclusions We explored the potential of the smartphone-based endoscopic VIA as an evaluation technique and used the cervicogram to evaluate normal/abnormal tissue using machine learning techniques. The results of this study demonstrate its potential as a screening tool in low-resource settings.

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Section: Principal Resultsmentioning

confidence: 99%

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Bae

Roh

You

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Functional optical imaging such as Laser speckle contrast imaging (LSCI) [1][2][3][4], optical intrinsic signal (OIS) imaging [5][6][7][8][9], and fluorescence imaging [10], which have the advantages of real time, wide-field and non-contact, have been widely attempted in clinic applications. Switching the illuminating sources in time sequence is widely used to realize multimodal imaging [11][12][13][14][15][16]. This method sacrifices temporal resolution so that the rapidly changing signals is difficult to be resolved.…”

Section: Introductionmentioning

confidence: 99%

Extendable, large-field multi-modal optical imaging system for measuring tissue hemodynamics

Wang¹,

Chen²,

Hong³

et al. 2020

Biomed. Opt. Express

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Simultaneous imaging of multiple hemodynamic parameters helps to evaluate the physiological and pathological status of biological tissue. To achieve multimodal hemodynamics imaging with a large field of view, an infinite conjugate relay lens system compatible with the standard C-Mount camera lens is designed to adapt one camera lens with multiple CCD/CMOS cameras for simultaneously multi-wavelength imaging. Using this relay lens system, dual wavelength reflectance imaging and laser speckle contrast imaging were combined to simultaneously detect the changes in blood flow, oxygenation, and hemoglobin concentrations. To improve the accuracy of hemoglobin concentration measurement with an LED illumination source, an integral algorithm is proposed that accounts for the dependence of differential pathlength factors (DPF) on hemoglobin concentrations and the integral effect of both the emission spectrum of the light source and the spectrum response of the detector. The imaging system is validated by both phantom and in vivo experiments, including the arterial occlusion, and the detection of blood volume pulse (BVP) and blood flow pulse (BFP) signal in human subjects. The system helps in the exploration of macroscopic tissue hemodynamics.

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“…To further explore MSI, some researchers combine reflectance MSI with autofluorescence to investigate the combined value of both. Ren and coworkers proposed to combine multispectral reflectance, autofluorescence, and RGB imaging for noninvasive characterization of cervical intraepithelial neoplasia (CIN) [9]. Their preliminary clinical results indicate that the added value of autofluorescence to the reflectance MSI is arguable considering the economic cost of the device and the additional operation in a clinical setting.…”

Section: Introductionmentioning

confidence: 99%

Contrast-Enhancing Snapshot Narrow-Band Imaging Method for Real-Time Computer-Aided Cervical Cancer Screening

Kong

Zhao

2019

J Digit Imaging

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Composition of cervical precancerous lesions and carcinoma in situ is rich in hemoglobin, unlike healthy tissues. In this study, we aimed to utilize this difference to enhance the contrast between healthy and diseased tissues via snapshot narrow-band imaging (SNBI). Four narrow-band images centered at wavelengths of characteristic absorption/reflection peaks of hemoglobin were captured with zero-time delay in between by a custom-designed SNBI video camera. Then these spectral images were fused in real time into a single combined image to enhance the contrast between normal and abnormal tissues. Finally, a Euclidean distance algorithm was employed to classify the tissue into clinical meaningful tissue types. Two pre-clinical experiments were conducted to validate the proposed method. Experimental results indicate that contrast between different grades of diseased tissues in the SNBI generated image was indeed enhanced, as compared to conventional white light image (WLI). The computeraided classification accuracy was 100% and 50% as compared to the gold standard histopathological diagnosis results with the SNBI and the conventional WLI methods, respectively. Further, the boundary contour between health tissue, cervical precancerous regions, and carcinoma in situ can be automatically delineated in SNBI. The proposed SNBI method was also fast, and it generated automatic diagnostic results with clear boundary contours at over 11 fps on a Pentium 1.6-GHz laptop. Hence, the proposed SNBI is of great significance to enlarge worldwide the coverage of regular cervical screening program, and to live guide surgeries such as biopsy sample collection and accurate cervical cancer treatment.

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Development of a Multimodal Colposcopy for Characterization of Cervical Intraepithelial Neoplasia

Cited by 4 publications

References 30 publications

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Extendable, large-field multi-modal optical imaging system for measuring tissue hemodynamics

Contrast-Enhancing Snapshot Narrow-Band Imaging Method for Real-Time Computer-Aided Cervical Cancer Screening

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