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

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

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions

A Portable System for Screening of Cervical Cancer

Cervical cancer is one of the most common cancers that affect women, with the highest incidence and mortality rates occurring in low- and middle-income countries. Early detection is crucial for successful treatment, but the need for expensive equipment, trained colposcopists, and clinical infrastructure has made it difficult to eradicate this disease. Accurately determining the size and location of a precancerous lesion involves specialized and costly equipment, making it difficult to track the progression of the disease or the efficacy of treatment. Imaging and machine learning techniques have been attempted by several researchers to overcome these limitations, but the subjective nature of diagnosis and other challenges persist. Therefore, there is a need to develop a system to automatically segment lesions on the cervix and quantify their size in relation to the cervical region of interest. Challenges to the automated detection of cervical cancer include:• Low quality of the devices used, which impair the image resolution; lighting conditions, which can make shadows appear, hindering the ability to find the cervical region of interest (ROI); distortion of the images due to the presence of glare or specular reflections (SR) from the light source; and the appearance of artifacts such as the speculum and surrounding tissue. The limitations that exist in selecting or designing a device to acquire cervical images (cervigrams) have been investigated. • The acquisition of cervical images requires access to sensitive patient information, which raises concerns about patient privacy and data security. Ensuring that patient data is protected and used only for diagnostic purposes is critical to building patient trust and ensuring widespread adoption of automated screening technologies. A pilot study to capture cervigrams from women that present early signs of cervical cancer was designed. Relevant data would be collected to further understand the progression of this disease, while maintaining privacy and confidentiality of the participants in the study. • The early detection of cervical cancer requires analyzing complex data, including images, pathology reports, and medical records. Automating the analysis of this data requires machine learning algorithms or image processing techniques capable of interpreting such information. Image processing methods based on traditional and machine learning techniques were leveraged to identify the cervical region of interest and remove light reflections from the cervical epithelium. Lesions present on the cervix were detected and their size, invariant with respect to the orientation of the camera or its distance from the cervix, was calculated. • Finally, variability and subjectivity are involved when acquiring and analyzing cervigrams. A graphical user interface was developed to facilitate data collection and analysis throughout the pilot study and future clinical trials. Results indicate that it is possible to segment images of the cervix, reduce the effect of glare from light sources, remove specular reflections and other artifacts, and successfully detect and quantify lesions through the proposed methods. The above approaches are demonstrated throughout this dissertation to show that a low-cost bioinformatics-based tool for early detection of cervical cancer can be achieved for screening patients in a clinical setting. While the algorithms used for analysis were validated using sample images from public databases, it is crucial to conduct small-scale clinical trials to further validate these methods. Furthermore, the use of more advanced image processing techniques or machine learning algorithms to improve the accuracy and speed of lesion detection is under review

Increasing Cervical Cancer Screening in a Federally Qualified Health Center

abstract: Routine cervical cancer screening has significantly decreased the mortality rate of cervical cancer. Today, cervical cancer predominantly affects those who are rarely or never screened. Government programs are in place to provide cervical cancer screening at little to no cost, yet screening rates remain suboptimal. This project evaluated an evidence-based intervention to increase cervical cancer screening among underserved women in a federally qualified health center (FQHC). Female patients ages 21 to 65 years without history of hysterectomy (n=1,710) were sent reminders to their phones through the electronic health record (EHR). The message included educational material about the screening process and an announcement regarding government aid for free or reduced cost screening. The number of patients who made an appointment after receiving the message was assessed two months later. In total, 156 responses were collected, and 28 patients made an appointment for screening. The most frequently observed category of Ethnicity was Hispanic/Latina (n = 24, 86%). The most frequently observed category of Insurance was Title X (n = 13, 46%). The observations for Age had an average of 41.04 (SD = 9.93). Using an EHR communication function to send motivational reminders has shown some promise for increasing cervical cancer screening, thereby reducing cervical cancer mortality among the underserved

Data fusion techniques for biomedical informatics and clinical decision support

Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. The general framework for these applications includes image processing followed by feature development and classification of the detected region of interest (ROI). Image processing methods such as k-means clustering based on color information, dilation, erosion and centroid locating methods were used for ROI detection. The features extracted include texture, color, nuclei-based and triangle features. Analysis and classification was performed using feature- and decision-level data fusion techniques such as support vector machine, statistical methods such as logistic regression, linear discriminant analysis and voting algorithms --Abstract, page iv

Clinical decision support system, a potential solution for diagnostic accuracy improvement in oral squamous cell carcinoma: A systematic review

BACKGROUND AND AIM: Oral squamous cell carcinoma (OSCC) is a rapidly progressive disease and despite the progress in the treatment of cancer, remains a life-threatening illness with a poor prognosis. Diagnostic techniques of the oral cavity are not painful, non-invasive, simple and inexpensive methods. Clinical decision support systems (CDSSs) are the most important diagnostic technologies used to help health professionals to analyze patients’ data and make decisions. This paper, by studying CDSS applications in the process of providing care for the cancer patients, has looked into the CDSS potentials in OSCC diagnosis. METHODS: We retrieved relevant articles indexed in MEDLINE/PubMed database using high-quality keywords. First, the title and then the abstract of the related articles were reviewed in the step of screening. Only research articles which had designed clinical decision support system in different stages of providing care for the cancer patient were retained in this study according to the input criteria. RESULTS: Various studies have been conducted about the important roles of CDSS in health processes related to different types of cancer. According to the aim of studies, we categorized them into several groups including treatment, diagnosis, risk assessment, screening, and survival estimation. CONCLUSION: Successful experiences in the field of CDSS applications in different types of cancer have indicated that machine learning methods have a high potential to manage the data and diagnostic improvement in OSCC intelligently and accurately. KEYWORDS: Squamous Cell Carcinoma; Clinical Decision Support System; Neoplasm; Dental Informatic

A clinician-mediated, longitudinal tracking system for the follow-up of clinical results

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references (p. 36-37).Failure to follow-up on abnormal tests is a common clinical concern comprising the quality of care. Although many clinicians track their patient follow-up by scheduling follow-up visits or by leaving physical reminders, most feel that automated, computerized systems to track abnormal test results would be useful. While existing clinical decision support systems and computerized clinical reminders focus on providing assistance with choosing the appropriate follow-up management, they fail by not tracking that follow-up effectively. We believe that clinicians do not want suggestions how to manage their patients, but instead want help tracking follow-up results once they have decided the management plan. We believe that a well-designed system can successfully track this follow-up and only require a small amount of information and time from the clinician. We have designed and implemented a complete tracking system including 1) an authoring tool to define tracking guidelines, 2) a query tool to search electronic medical records and identify patients without follow-up, and 3) a clinical tool to send reminders to clinicians and allow them to easily choose the follow-up management. Our tracking system has made improvements on previous reminder systems by 1) using our unique risk-management guideline model that more closely mirrors, yet does not attempt to replicate, the clinical decision process, 2) our use of massive population-based queries for tracking all patients simultaneously, and 3) our longitudinal approach that documents all steps in the patient follow-up cycle. With these developments, we are able to track 450 million pieces of clinical data for 1.8 million patients daily.(cont.) Keyword follow-up tracking; reminder system; preventive medicine; computerized medical record system; practice guidelines; clinical decision support systemby Daniel Todd Rosenthal.S.M

Examining the language demands of informed consent documents in patient recruitment to cancer trials using tools from corpus and computational linguistics

Obtaining informed consent (IC) is an ethical imperative, signifying participants’ understanding of the conditions and implications of research participation. One setting where the stakes for understanding are high is randomized controlled trials (RCTs), which test the effectiveness and safety of medical interventions. However, the use of legalese and medicalese in ethical forms coupled with the need to explain RCT-related concepts (e.g. randomization) can increase patients’ cognitive load when reading text. There is a need to systematically examine the language demands of IC documents, including whether the processes intended to safeguard patients by providing clear information might do the opposite through complex, inaccessible language. Therefore, the goal of this study is to build an open-access corpus of patient information sheets (PIS) and consent forms (CF) and analyze each genre using an interdisciplinary approach to capture multidimensional measures of language quality beyond traditional readability measures. A search of publicly-available online IC documents for UK-based cancer RCTs (2000-17) yielded corpora of 27 PIS and 23 CF. Textual analysis using the computational tool, Coh-Metrix, revealed different linguistic dimensions relating to the complexity of IC documents, particularly low word concreteness for PIS and low referential and deep cohesion for CF, although both had high narrativity. Key part-of-speech analyses using Wmatrix corpus software revealed a contrast between the overrepresentation of the pronoun ‘you’ plus modal verbs in PIS and ‘I’ in CF, exposing the contradiction inherent in conveying uncertainty to patients using tentative language in PIS while making them affirm certainty in their understanding in CF