Abstract
[Background]
Cervical cancer is one of the most common types of cancer among women, caused by thehuman papillomavirus (HPV) infection, commonly types 16 and 18 (high-risk types). Globally, there were approximately 570,000 cases and 311,000 deaths in 2018, with the incidence ranging around 13.1 per 100,000 women and varied widely among countries. In Indonesia, there were 36,633 new cases and 21,003 deaths reported in 2020, making it the second most common malignancy in women after breast cancer. Routine screening, especially in high-risk populations, is a key to prevent disease progression by prompt treatment at an earlier stage. The visual inspection with acetic acid (VIA) is considered costeffective and widely available as a practical test for the national cervical cancer screening program. The test is also considered to be safe and can be done by trained health workers. However, interpretation of the result requires skills and is highly subjective. Thus, the result’s interpretations may be slightly different among workers. Emerging artificial intelligence technology may provide the solution to minimize the variability of those results. However, designing such technology requires big, validated data. Therefore, this research aims to build a web-based medical image repository system to make it easier to collect and manage VIA-test images dataset for creating and training such robust artificial intelligence technology. The system’s key feature lies in the automatic voted image categorization system, of which there were three categories (positive, negative, or inconclusive) based on the ‘vote’ (label) judgement given to the image.
[Methods]
The system was built on Laravel PHP framework version 7 and deployed on Intel® Xeon® CPU E5-2640 @2.40 GHz, 6 gigabytes of RAM server running Ubuntu 20.04 LTS, PHP 7.4.3, MySQL 8.0.23, and NginX 1.18.0. There were 868 collected unlabeled VIA-test images from our database stored on the system. We involved ten medical consultants from the Department of Obstetrics and Gynecology, Dr. Cipto Mangunkusumo National General Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta. Those images were further divided into five randomized groups, with each group consisting of around 173 images. Each group of images would be interpreted and graded by two randomized consultants based on the VIA-test result, whether it is acetowhite- positive or negative, through the system. If the result was inconclusive, defined by the previous consultants' different interpretations (positive-negative or negative-positive), a third independent consultant would be involved in grading the image. The gradings were conducted from August to December of 2020.
[Results]
The system was named the “Simple Acetowhite Labeling” website, and the source code was available open for access through CerviCam Research Group GitHub Repository (https://github.com/CerviCam/Simple-Acetowhite-Labeling-Web-Application). After image grading process by the consultants, 420 images were categorized as positive VIA-test, while the 448 rest were categorized as negative VIA-test. The categorized images could be archived together and downloaded as a dataset (positive or negative VIA- test dataset) through the system menu. The dataset was then further used to train artificial intelligence models for interpreting results of VIA-test images.
[Discussion]
The “Simple Acetowhite Labeling” system provides practicality in accessing and categorizing VIA-test image collection. Being a website-based platform, the system is accessible through internet from any physical location. The system also provides ease of adaptability for medical image repository for other health institutions.Other than the aforementioned advantages, one of the main challenges during the system development was in designing a clear and intuitive user interface that was easily understood by the consultants as the users. Creating a user journey guideline and planning an intensive training session for the consultants are deemed necessary to overcome this challenge. Moreover, the system is also dependent on expert opinions which is subjective by nature. Consequently, objective data validation could not be guaranteed. Further research should be conducted to develop a more objective data validation mechanism.
[Conclusions]
The development of the “Simple Acetowhite Labeling” system as a web-based medical image repository system is an initial step toward creating artificial intelligence technology in automatizing VIA-test result's interpretation. The system was designed to manage VIAtest image collection and further generate datasets that could be used to create and train artificial intelligence models.
Keywords: Cervix Uteri, Uterine Cervical Neoplasms, Visual Inspection with Acetic Acid, Medical Informatics Application
Cervical cancer is one of the most common types of cancer among women, caused by thehuman papillomavirus (HPV) infection, commonly types 16 and 18 (high-risk types). Globally, there were approximately 570,000 cases and 311,000 deaths in 2018, with the incidence ranging around 13.1 per 100,000 women and varied widely among countries. In Indonesia, there were 36,633 new cases and 21,003 deaths reported in 2020, making it the second most common malignancy in women after breast cancer. Routine screening, especially in high-risk populations, is a key to prevent disease progression by prompt treatment at an earlier stage. The visual inspection with acetic acid (VIA) is considered costeffective and widely available as a practical test for the national cervical cancer screening program. The test is also considered to be safe and can be done by trained health workers. However, interpretation of the result requires skills and is highly subjective. Thus, the result’s interpretations may be slightly different among workers. Emerging artificial intelligence technology may provide the solution to minimize the variability of those results. However, designing such technology requires big, validated data. Therefore, this research aims to build a web-based medical image repository system to make it easier to collect and manage VIA-test images dataset for creating and training such robust artificial intelligence technology. The system’s key feature lies in the automatic voted image categorization system, of which there were three categories (positive, negative, or inconclusive) based on the ‘vote’ (label) judgement given to the image.
[Methods]
The system was built on Laravel PHP framework version 7 and deployed on Intel® Xeon® CPU E5-2640 @2.40 GHz, 6 gigabytes of RAM server running Ubuntu 20.04 LTS, PHP 7.4.3, MySQL 8.0.23, and NginX 1.18.0. There were 868 collected unlabeled VIA-test images from our database stored on the system. We involved ten medical consultants from the Department of Obstetrics and Gynecology, Dr. Cipto Mangunkusumo National General Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta. Those images were further divided into five randomized groups, with each group consisting of around 173 images. Each group of images would be interpreted and graded by two randomized consultants based on the VIA-test result, whether it is acetowhite- positive or negative, through the system. If the result was inconclusive, defined by the previous consultants' different interpretations (positive-negative or negative-positive), a third independent consultant would be involved in grading the image. The gradings were conducted from August to December of 2020.
[Results]
The system was named the “Simple Acetowhite Labeling” website, and the source code was available open for access through CerviCam Research Group GitHub Repository (https://github.com/CerviCam/Simple-Acetowhite-Labeling-Web-Application). After image grading process by the consultants, 420 images were categorized as positive VIA-test, while the 448 rest were categorized as negative VIA-test. The categorized images could be archived together and downloaded as a dataset (positive or negative VIA- test dataset) through the system menu. The dataset was then further used to train artificial intelligence models for interpreting results of VIA-test images.
[Discussion]
The “Simple Acetowhite Labeling” system provides practicality in accessing and categorizing VIA-test image collection. Being a website-based platform, the system is accessible through internet from any physical location. The system also provides ease of adaptability for medical image repository for other health institutions.Other than the aforementioned advantages, one of the main challenges during the system development was in designing a clear and intuitive user interface that was easily understood by the consultants as the users. Creating a user journey guideline and planning an intensive training session for the consultants are deemed necessary to overcome this challenge. Moreover, the system is also dependent on expert opinions which is subjective by nature. Consequently, objective data validation could not be guaranteed. Further research should be conducted to develop a more objective data validation mechanism.
[Conclusions]
The development of the “Simple Acetowhite Labeling” system as a web-based medical image repository system is an initial step toward creating artificial intelligence technology in automatizing VIA-test result's interpretation. The system was designed to manage VIAtest image collection and further generate datasets that could be used to create and train artificial intelligence models.
Keywords: Cervix Uteri, Uterine Cervical Neoplasms, Visual Inspection with Acetic Acid, Medical Informatics Application