A Systematic Review of Oral Biopsies, Sample Types, and Detection Techniques Applied in Relation to Oral Cancer Detection
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SUBMITTER: Yang G
PROVIDER: S-EPMC9245907 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNormal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems.ObjectiveThis study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest.MethodsThis systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group.ResultsIn the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models.ConclusionsThis SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.
| S-EPMC10398366 | biostudies-literature
Project description:Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.
| S-EPMC10572538 | biostudies-literature
Project description:Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
| S-EPMC8201065 | biostudies-literature
Project description:Different efforts have been made to find better and less invasive methods for the diagnosis and prediction of oral cancer, such as the study of saliva as a source of biomarkers. The aim of this study was to perform a scoping review about salivary molecules that have been assessed as possible biomarkers for the diagnosis of oral squamous cell carcinoma (OSCC). A search was conducted using EBSCO, PubMed (MEDLINE), Scopus, and Web of Science. The research question was as follows: which molecules present in saliva have utility to be used as biomarkers for the early detection of oral cancer? Sixty-two studies were included. Over 100 molecules were assessed. Most of the markers were oriented towards the early diagnosis of OSCC and were classified based on their ability for detecting OSCC and oral potentially malignant disorders (OPMDs), OSCC outcome prediction, and the prediction of the malignant transformation of OPMDs. TNF-α, IL-1β, IL-6 IL-8, LDH, and MMP-9 were the most studied, with almost all studies reporting high sensitivity and specificity values. TNF-α, IL-1β, IL-6 IL-8, LDH, and MMP-9 are the most promising salivary biomarkers. However, more studies with larger cohorts are needed before translating the use of these biomarkers to clinical settings.
| S-EPMC10932009 | biostudies-literature
Project description:BackgroundThis systematic review investigates techniques for determining adequate mucosal margins during the resection of oral squamous cell carcinoma (SCC). The primary treatment involves surgical removal with ≥5 mm margins, highlighting the importance of accurate differentiation between SCC and dysplasia during surgery.MethodsA comprehensive Embase and PubMed literature search was performed. Studies underwent quality assessment using QUADAS-2.ResultsAfter the full-text screening and exclusion of studies exhibiting high bias, eight studies were included, focusing on three margin visualization techniques: autofluorescence, iodine staining, and narrow-band imaging (NBI). Negative predictive value (NPV) was calculable across the studies, though reference standards varied. Results indicated NPVs for autofluorescence, iodine, and NBI ranging from 61% to 100%, 92% to 99%, and 86% to 100%, respectively. Autofluorescence did not significantly enhance margins compared to white light-guided surgery, while iodine staining demonstrated improvement for mild or moderate dysplasia. NBI lacked comparison with a white light-guided surgery cohort.ConclusionsWe recommend studying and comparing the diagnostic accuracy of iodine staining and NBI in larger cohorts of patients with oral SCC, focusing on discriminating between SCC and (severe) dysplasia. Furthermore, we advise reporting the diagnostic accuracy alongside the treatment effects to improve the assessment of these techniques.
| S-EPMC10968727 | biostudies-literature
Project description:BackgroundMore than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care.ObjectiveThis study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated.MethodsWe searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer.ResultsWe identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures.ConclusionsAI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.
| S-EPMC7970165 | biostudies-literature
Project description:BackgroundA wide variety of surgical techniques are used to perform vasectomy. The purpose of this systematic review was to assess if any surgical techniques to isolate or occlude the vas are associated with better outcomes in terms of occlusive and contraceptive effectiveness, and complications.MethodsWe searched MEDLINE (1966-June 2003), EMBASE (1980-June 2003), reference lists of retrieved articles, urology textbooks, and our own files looking for studies comparing two or more vasectomy surgical techniques and reporting on effectiveness and complications. From 2,058 titles or abstracts, two independent reviewers identified 224 as potentially relevant. Full reports of 219 articles were retrieved and final selection was made by the same two independent reviewers using the same criteria as for the initial selection. Discrepancies were resolved by involving a third reviewer. Data were extracted and methodological quality of selected studies was assessed by two independent reviewers. Studies were divided in broad categories (isolation, occlusion, and combined isolation and occlusion techniques) and sub-categories of specific surgical techniques performed. Qualitative analyses and syntheses were done.ResultsOf 31 comparative studies (37 articles), only four were randomized clinical trials, most studies were observational and retrospective. Overall methodological quality was low. From nine studies on vas isolation, there is good evidence that the no-scalpel vasectomy approach decreases the risk of surgical complications, namely hematoma/bleeding and infection, compared with incisional techniques. Five comparative studies including one high quality randomized clinical trial provided good evidence that fascial interposition (FI) increases the occlusive effectiveness of ligation and excision. Results of 11 comparative studies suggest that FI with cautery of the vas lumen provides the highest level of occlusive effectiveness, even when leaving the testicular end open. Otherwise, firm evidence to support any occlusion technique in terms of increased effectiveness or decreased risk of complications is lacking.ConclusionsCurrent evidence supports no-scalpel vasectomy as the safest surgical approach to isolate the vas when performing vasectomy. Adding FI increases effectiveness beyond ligation and excision alone. Occlusive effectiveness appears to be further improved by combining FI with cautery. Methodologically sound prospective controlled studies should be conducted to evaluate specific occlusion techniques further.
| S-EPMC428590 | biostudies-literature
Project description:We investigate and analyze methods to violence detection in this study to completely disassemble the present condition and anticipate the emerging trends of violence discovery research. In this systematic review, we provide a comprehensive assessment of the video violence detection problems that have been described in state-of-the-art researches. This work aims to address the problems as state-of-the-art methods in video violence detection, datasets to develop and train real-time video violence detection frameworks, discuss and identify open issues in the given problem. In this study, we analyzed 80 research papers that have been selected from 154 research papers after identification, screening, and eligibility phases. As the research sources, we used five digital libraries and three high ranked computer vision conferences that were published between 2015 and 2021. We begin by briefly introducing core idea and problems of video-based violence detection; after that, we divided current techniques into three categories based on their methodologies: conventional methods, end-to-end deep learning-based methods, and machine learning-based methods. Finally, we present public datasets for testing video based violence detectionmethods' performance and compare their results. In addition, we summarize the open issues in violence detection in videoand evaluate its future tendencies.
| S-EPMC9044356 | biostudies-literature
Project description:Education and other strategies to promote optimal complementary feeding can significantly improve practices, but little is known about the specific techniques successful interventions use to achieve behaviour change. We reviewed the literature for complementary feeding interventions in low-/middle-income countries (LMIC) published since 2000. We systematically applied a validated taxonomy mapping process to code specific behaviour change techniques (BCTs) used in each intervention; effectiveness ratios for each BCT were estimated. Sixty-four interventions met inclusion criteria, were abstracted, BCTs identified, and coded. Dietary diversity was the most commonly assessed component of complementary feeding, and interpersonal communication, either individually or in groups, was the most commonly used delivery platform. Of the 93 BCTs available for mapping, the 64 interventions included in this review applied a total of 28 BCTs. Interventions used a median of six techniques (max = 13; min = 2). All interventions used "instruction on how to perform the behaviour." Other commonly applied BCTs included "use of a credible source" (n = 46), "demonstration of the behaviour" (n = 35), and "providing information about health consequences" (n = 30). Forty-three interventions reported strategies to shift the physical or social environment. Among BCTs used in >20 interventions, five had effectiveness ratios >0.8: "provision of/enabling social support"; "providing information about health consequences"; "demonstration of the behaviour"; and "adding objects to the environment" namely, food, supplements, or agricultural inputs. The limited reporting of theory-based BCTs in complementary feeding interventions may impede efforts to improve and scale effective programs and reduce the global burden of malnutrition.
| S-EPMC7038900 | biostudies-literature
Project description:Two efforts to improve the sensitivity and limits of detection for MCE with electrochemical detection are presented here. One is the implementation of a capillary expansion (bubble cell) at the detection zone to increase the exposed working electrode surface area. Bubble cell widths were varied from 1x to 10x the separation channel width (50 mum) to investigate the effects of electrode surface area on detection sensitivity, LOD, and separation efficiency. Improved detection sensitivity and decreased detection limits were obtained with increased bubble cell width, and LODs of dopamine and catechol detected in a 5x bubble cell were 25 and 50 nM, respectively. Meanwhile, fluorescent imaging results demonstrated approximately 8 and approximately 12% loss in separation efficiency in 4x and 5x bubble cell, respectively. Another effort at reducing the LOD involves using field amplified sample injection for gated injection and field amplified sample stacking for hydrodynamic injection. Stacking effects are shown for both methods using amperometric detection and pulsed amperometric detection. The LODs of dopamine in a 4x bubble cell were 8 and 20 nM using field amplified sample injection and field amplified sample stacking, respectively. However, improved LODs were not obtained for anionic analytes using either stacking technique.
| S-EPMC3005344 | biostudies-literature