Colposcopy audit for improving quality of service in areas with a high incidence of cervical cancer

Manopunya, Manatsawee; Suprasert, Prapaporn; Srisomboon, Jatupol; Kietpeerakool, Chumnan

doi:10.1016/j.ijgo.2009.07.042

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Colposcopy is an important method for evaluating women with abnormal cervical screening results [1]. The predominant advantage of immediate colposcopy among women with abnormal cervical screening results is the early detection and treatment of underlying cervical lesions.…”

Section: Introductionmentioning

confidence: 99%

Factors affecting compliance in the first year of postcolposcopy surveillance among women with a high incidence of cervical cancer

Rattanalappaiboon

Kietpeerakool

Kleebkaow

et al. 2013

International Journal of Gynecology & Obstetrics

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Section: Introductionmentioning

confidence: 99%

Factors affecting compliance in the first year of postcolposcopy surveillance among women with a high incidence of cervical cancer

Rattanalappaiboon

Kietpeerakool

Kleebkaow

et al. 2013

International Journal of Gynecology & Obstetrics

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“…Colposcopy is an important imaging modality for the diagnosis and classification of cervical lesions [ 12 13 ]; therefore, it also plays a central role in cervical cancer prevention. During biopsy, the gynecologist would use a colposcope to manually examine the uterine cervix to determine the site for procuring the tissue samples for a complete microscopic evaluation [ 14 15 ]. However, determining the lesion of the cervical tissue that should be obtained relies on subjective assessment and requires substantial practical expertise of the gynecologist, thereby limiting the accuracy and reproductivity of colposcopy [ 16 17 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis

et al. 2022

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Objective Human papillomavirus subtypes are predictive indicators of cervical intraepithelial neoplasia (CIN) progression. While colposcopy is also an essential part of cervical cancer prevention, its accuracy and reproducibility are limited because of subjective evaluation. This study aimed to develop an artificial intelligence (AI) algorithm that can accurately detect the optimal lesion associated with prognosis using colposcopic images of CIN2 patients by utilizing objective AI diagnosis. Methods We identified colposcopic findings associated with the prognosis of patients with CIN2. We developed a convolutional neural network that can automatically detect the rate of high-grade lesions in the uterovaginal area in 12 segments. We finally evaluated the detection accuracy of our AI algorithm compared with the scores by multiple gynecologic oncologists. Results High-grade lesion occupancy in the uterovaginal area detected by senior colposcopists was significantly correlated with the prognosis of patients with CIN2. The detection rate for high-grade lesions in 12 segments of the uterovaginal area by the AI system was 62.1% for recall, and the overall correct response rate was 89.7%. Moreover, the percentage of high-grade lesions detected by the AI system was significantly correlated with the rate detected by multiple gynecologic senior oncologists (r=0.61). Conclusion Our novel AI algorithm can accurately determine high-grade lesions associated with prognosis on colposcopic images, and these results provide an insight into the additional utility of colposcopy for the management of patients with CIN2.

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“…A colposcopy is a test that allows the cervix to be viewed with up to 10 times magnification, 1,2 which is essential for the evaluation of abnormalities or lesions (which are highly suggestive of cervical neoplasia) 3,4 during cytopathological examinations and to assist in biopsies (which are essential for definitive diagnoses). 5,6 Infection with human papillomavirus (HPV) is a sexually transmitted disease and is considered a preneoplastic lesion. The incidence of HPV infection has increased in recent decades.…”

Section: Introductionmentioning

confidence: 99%

Classification of Images Acquired with Colposcopy Using Artificial Neural Networks

et al. 2014

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OBJECTIVETo explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy.PURPOSETransversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used.RESULTSAfter 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%.CONCLUSIONAlthough the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.

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Colposcopy audit for improving quality of service in areas with a high incidence of cervical cancer

Abstract: Colposcopic performance at Chiang Mai University Hospital was generally favorable. However, re-audit is necessary to ensure that unmet standards of performance are improved and achieved standards are maintained.

Cited by 6 publications

References 14 publications

Factors affecting compliance in the first year of postcolposcopy surveillance among women with a high incidence of cervical cancer

Factors affecting compliance in the first year of postcolposcopy surveillance among women with a high incidence of cervical cancer

Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis

Classification of Images Acquired with Colposcopy Using Artificial Neural Networks

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