dataesr/scientific-paragraphs-categorization huggingface dataset:
Survey Instrument The questionnaire consisted of close-ended questions divided into five sections. The first collected sociodemographic data (age, sex, level of education, profession). The second investigated oral health behavioral data (brushing technique, frequency, and bristle hardness of toothbrush). The third explored the GB: presence (yes/no) and trigger factor (spontaneously/during teeth brushing). The fourth assessed dental trait anxiety perceived by the patient interviewed using the dental anxiety scale (DAS)  [17, 18] , which has been shown to be one of the more inclusive, highly validated, and reliable scales. Responses to the four items of the scale were scored from 1 to 5, producing total scores ranging from 4 to 20. Individuals were considered as not anxious for a DAS score <9, moderately anxious for a DAS score between 9 and 12, and highly/severely anxious for a DAS score ≥13  [17].
Statistical Analyses The sample size was calculated with respect to the multiple logistic regression model which was used in the statistical analyses. According to Peduzzi et al. [19] , the minimum number of cases to include is equal to N = 10 k/p, k being the number of covariates in the regression model and p being the smallest of the proportions of positive or negative cases in the population (here the prevalence of GB). With k = 20 (the number of covariates in the regression model) and p = 0. 30 (the most common prevalence of GB found in the literature), n = 666. Considering the proportion of potentially erroneous or incomplete questionnaires, the estimated sample size was 800. Therefore, we included 200 subjects in each of the four towns. All data were collected on Microsoft ® Excel (Microsoft Corporation, Redmond, WA, USA) and analyzed using SAS ® software version 9.
This systematic review analyses and describes the application and diagnostic accuracy of Artificial Intelligence (AI) methods used for detection and grading of potentially malignant (pre-cancerous) and cancerous head and neck lesions using whole slide images (WSI) of human tissue slides. Electronic databases MEDLINE via OVID, Scopus and Web of Science were searched between October 2009 -April 2020. Tailored search-strings were developed using database-specific terms. Studies were selected using a strict inclusion criterion following PRISMA Guidelines. Risk of bias assessment was conducted using a tailored QUADAS-2 tool. Out of 315 records, 11 fulfilled the inclusion criteria. AI-based methods were employed for analysis of specific histological features for oral epithelial dysplasia (n=1), oral submucous fibrosis (n=5), oral squamous cell carcinoma (n=4) and oropharyngeal squamous cell carcinoma (n=1).
A combination of heuristics, supervised and unsupervised learning methods were employed, including more than 10 different classification and segmentation techniques. Most studies used uni-centric datasets (range 40-270 images) comprising small sub-images within WSI with accuracy between 79-100%. This review provides early evidence to support the potential application of supervised machine learning methods as a diagnostic aid for some oral potentially malignant and malignant lesions; however, there is a paucity of evidence using AI for diagnosis of other head and neck pathologies. Overall, the quality of evidence is low, with most studies showing a high risk of bias which is likely to have overestimated accuracy rates. This review highlights the need for development of state-of-the-art deep learning techniques in future head and neck research.
Literature search Electronic databases search of MEDLINE via OVID, Scopus and Web of Science was conducted to retrieve articles published in the English language between October 2009 and April 2020. The Cochrane library was also consulted. This time period was chosen due to the rapid evolution of AI methods and their application to cancer diagnostics over the last decade. The search strategy was jointly developed by the multidisciplinary authorship team in collaboration with a medical information specialist (librarian from University of Sheffield, UK). Input from an expert Oral and Maxillofacial Pathologist (SAK, University of Sheffield, UK) and a Professor of Computational Pathology (NMR, University of Warwick, UK) ensured adequate selection of clinical and technical terms and controlled vocabulary for optimal identification of articles.
Tailored search strings containing keywords and database-specific medical subject headings (MeSH) for the two major topics (AI/ML and potentially malignant or cancerous head and neck disorders/lesions) were developed. Multiple variations of search terms were combined to produce different sets of results and the final search strategy was pilot-tested and modified accordingly (Appendix 1). Grey literature and the reference lists of selected articles were also screened for relevant studies that may not have been identified through the database searches. The electronic databases search was conducted with assistance from an experienced Librarian at the University of Sheffield, UK. Article citations were exported to EndNote® reference manager software (Clarivate Analytics, Philadelphia, USA) and duplicate articles were removed.
Study selection Two independent reviewers (HM, MS) retrieved the literature, and screened titles and abstracts. Where insufficient information was available to determine eligibility, the full report was obtained for further assessment. Articles that did not meet the eligibility criteria were excluded. In the second stage of study selection, the same two reviewers (HM, MS) independently assessed the full-text reports to obtain a shortlist of relevant articles. The shortlists were compared, and differences discussed, obtaining a final selection of studies. In case of any discrepancies in article selection, a discussion with senior members of the review team (NMR, SAK) took place to reach a mutual final decision. For relevant articles with overlapping datasets or results, the most recent publication was included.
Data extraction Relevant data from selected articles was extracted, processed and tabulated into a pre-developed data collection form in Microsoft Excel ® (Microsoft Corporation, Washington, USA) by two reviewers (HM, MS). The following information was recorded: • Study details (authors, year and country of publication, aims) • Study methods (design, dataset size and selection) • Description of outcome variables (AI/ML methods used, head and neck lesion and histological parameter under study, training and validation sample details) and its outcome measures (reported diagnostic accuracy, effect measures) • Other relevant details (funding information, sources of support, conflict of interest disclosure).
Search results The electronic database search retrieved a total of 314 articles (MEDLINE via OVID: 154, Scopus: 81 and Web of Science: 79). In addition, one article was identified through citation searching. Following removal of duplicate studies, 288 articles were selected. After the first screen based on title and abstract, 259 articles did not satisfy the inclusion criteria and were therefore excluded. A comprehensive full-text examination of the remaining 29 articles excluded a further 18; resulting in 11 eligible articles for inclusion in this review paper (Figure  1 ). Out of 315 articles, 277 were excluded, with a large proportion not eligible due to the use of imaging modalities other than histology (n=157). Many studies did not address the research question directly (n=67) as they focussed on the application of AI algorithms to predict disease prognosis, recurrence, metastasis or treatment success.
Detection of OSCC Das et al. 53  aimed to detect OSCC using a two-stage approach. This involved segmentation of constituent layers of the oral mucosa (into epithelial, subepithelial and keratin layers) followed by texture-based classification of keratin pearls from segmented keratin regions. The detection accuracy for keratin pearls was reported as 96. 88% however this was based on a small dataset comprising small patches (sub-images) within WSI. Rahman et al. 59  used a texture-based classifier to distinguish between normal and cancerous cells achieving an accuracy of 100% using small patches within WSI. In another study, Sun et al. 60  developed an automated colour-based feature extraction system to segment and classify OSCC stained with anti-CD34 antibody. Specific histological features (vessel area/number/density and nuclei area/number) were computed to enable quantitative differentiation between different OSCC stages.
Detection of OPSCC Fouad et al. 61  used unsupervised ML methods for automated identification of specific tissue compartments (cells and nuclei) in OPSCC tissue microarrays. Measurements of cell and nuclei colour and morphology were used for classification of epithelial and stromal tissue. This study compared their results with other standard segmentation methods and reported relatively low recognition accuracy (pixel-level F1 score of 80-81%) attributed to the lack of pre-defined manual annotations often used in supervised learning methods. ML methods used in selected studies ML algorithms can be divided into two groups: classical and modern. The classical methods require small amounts of training data and computational resources for pattern recognition in comparison to modern methods. However, modern methods often outperform classical methods in addressing most ML problems.
These nine methods differ significantly in their learning strategies, as outlined below: ▪ Sugeno Fuzzy 30 involves ML of fuzzy rules from the training dataset. ▪ Decision Tree 31 generates a binary tree based on training features for classification. ▪ Random Forest 32 builds a classification model using a set of decision tree-based classifiers. ▪ K-Nearest Neighbour  33  classifies an input image based on its similarity with other training set images, which enables the most dominant class of K to be assigned to the input image. ▪ Bayesian Classifier 34 use the Bayes rules to calculate the probability of an input sample to be a member of a specific class where the final label is assigned to the most probabilistic class for the given input image. ▪ Linear Discriminant Analysis  35   A combination of classification and segmentation methods were used to detect potentially malignant and cancerous head and neck lesions (Figure  3 ).
Overall, five studies used AI based classifiers  52, 54, 56, 59, 62  , four studies used segmentation methods  53, 58, 60, 61  and two studies used a combination of classification and segmentation methods  55, 57. The most frequently used methods included SVM  36  , Neural Network  38  , Random Forest  32  and clustering. In the majority of studies, multiple methods were used for intermediate and final stages of the proposed AI framework, although there were considerable variations in the overall number of methods used between studies. For example, in one study Krishnan et al. 54  compared five different classification approaches to obtain the best performing method, whereas in another study only two methods were trialled. 58 age datasets used in the selected studies The spatial dimensions of images ranged from 262,144 to 10,890,000 pixels; this excludes three studies where the image dimensions were not described  52, 56, 62.
Quality appraisal assessment The quality of selected studies was assessed using a tailored QUADAS-2 tool. The overall score for each study was determined by combining the number of satisfied criteria, with a higher score representing higher quality evidence (Table  3 ). There was considerable variability in the methodological quality of included studies. Baik et al. 52  scored the highest for including the use of a separate validation and test set for optimal model selection and evaluation  52  , whereas Sun et al. 60  scored the lowest across the 13 areas of assessment, largely due to the description of their approach without the use of reasonable dataset and results  60. In all applicable studies, except for Lorsakul et al. 56  , the methods and intermediate results were clearly presented.
DISCUSSION In order to safely and effectively implement automated AI-based methods in diagnostic and clinical practice, it is vital to validate these algorithms using a robust and fair experimental setup. This setup should include a clinically representative dataset and suitable evaluation metrics for validation. The ideal dataset should represent clinical practice and take into account the whole tissue section. Tissue samples from multiple centres will enable greater diversity and biological variance through inclusion of cases from different geographical locations, patient populations and demographics. Furthermore, the ground truth should include meticulous annotations from multiple pathologists to minimise subjectivity and take into account inter-pathologist variation. In this review, seven studies described more than one expert to be involved in providing ground truth, however it is not clear whether these refer to experienced pathologists, trainees, non-clinical researchers or other allied healthcare professionals.
In most cases, the number of experts involved has also not been clearly stated. Furthermore, multi-centric data and WSI were used in only two studies (Rahman et al. 59  and Lorsakul et al 56 respectively). The majority of studies therefore used unicentric datasets mostly comprising smaller sub-images within WSI, which may have introduced bias and would offer limited applicability. In supervised ML methods, the ideal dataset should be divided into three groups for 1) model training 2) optimal model selection and 3) validation or evaluation. In this review, most of the assessed studies used the same dataset for both optimal model selection and evaluation. This indicates a high risk of bias which is likely to have contributed to the high accuracy rates (ranging from 79-100% across all studies).
This bias could have been easily avoided by dividing these datasets into the three defined sub-sets or adding more unseen cases to the test and validation sets. The overall size of a dataset mainly depends on the type of AI method used. Traditional AI methods require small datasets whereas modern machine/deep learning methods require a larger dataset for model training. This concept is also known as the model generalisability and is concerned with the replication of model accuracy when applied to a new and diverse cohort of cases. Most of the reviewed studies used AI methods which were regarded as the state-of-the-art at the time of publication and described these in sufficient detail to ensure reproducibility. Various evaluation metrics were utilised (accuracy, sensitivity, specificity etc.
CONCLUSION This review provides early evidence to support application of supervised ML methods as an aid to detection and grading in a limited number and types of OPMD. Furthermore, there is limited evidence exploring the use of AI to aid diagnosis of other potentially premalignant and cancerous head and neck lesions. Having said this, most of the described AI/ML methods have the potential be modified for application to other clinical sites, including other head and neck lesions. The overall performance of the AI methods appears comparable to conventional light microscopic histopathological assessment but with added advantages of a faster, objective and reproducible evaluation. However, integration of these methods in the digital pathology workflow requires comprehensive evaluation of each method based on large multi-centric datasets.
36arns a linear combination of the features from training images to predict the label of test images. ▪Support Vector Model (SVM)36learns a set of parameters from the training image to find a hyperplane which splits the training images into two classes. Same parameters are then used to classify test images. ▪ Gaussian Mixtures Model 37 learns multiple models from the training images to classify it into multiple classes. ▪ Neural Network 38 methods learn the representation of the training images using a gradient descentbased learning method. These methods require large training datasets compared to other aforementioned supervised based learning methods.
gingivalis infection. (A) TNF-α secretion in supernatant by EC in response to P. gingivalis (Pg) (MOI:100) and EMV Pg (5, 20 and 30 nM) for 24 h was measured by ELISA. (B) Intra-cellular protein expression of eNOS, P21, ICAM-1, CDK4, P53, VCAM, iNOS and SOD-1 in response to P. gingivalis (Pg) (MOI:100) and EMV Pg (5, 20 and 30 nM) for 24 h was evaluated by Western Blot. All data were expressed as mean ± SD from 3 independent experiments and normalized against internal control β-actin. *p < 0. 05 between cells infected or stimulated against control (unstimulated cells). target cells. Similarly, human immunodeficiency virus also promotes MV shedding to transfer CCR5, its co-receptor on macrophages, to non-exposing target cells, consequently, promoting its own spreading  46. MV hijacking by pathogens could also be the causative of link between periodontitis and cardiovascular disease, as strongly supported by our data and previous reports  47.
Statistical Analyses Analyses were performed on the per protocol populations. An inferential analysis was performed for the comparison between toothbrushes: -For each product, a paired t-test was used to assess all changes from the baselines (D8-D1) and (D15-D1). The normality assumption was checked using a Shapiro-Wilk test (α=0. 01). In case of rejection, a Wilcoxon signed rank test was carried out instead. -For each change from the baselines (D8-D1) and (D15-D1), the comparison between the products was performed using an unpaired t-test (or a Mann-Whitney test). The probability of a type I error was set at p=0. 05 in bilateral mode and the statistical software used was Microsoft ® Excel 2010 and SAS ® 9. 4.
Results Of the 119 screened subjects, 9 subjects did not fulfil the inclusion/exclusion criteria and were not retained. Therefore, 110 subjects were enrolled in the study and randomized in two groups (Figure  3 ). The mean age was the same in both groups: 29±2 years. The numbers of [males/females] were [20/35] in the hybrid PTB and [17/  38] in the MTB. As a primary efficacy criterion, the plaque score was assessed in each group using TMQHPI. As illustrated in Tables  1  and 2  and Suppl. Figure  S1 , data showed that both brushes significantly reduced the mean plaque index over time irrespective of the localization (global/labial/lingual/1st and 2nd molars). When comparing their efficacy, a similar significant decrease in the mean TMQHP Index was observed on D8 for hybrid PTB and MTB (respectively -50% versus -48%; p=0.
Therefore, the tolerance of MTB was considered to be good at the clinical level. Three further reactions were considered to be related to the toothpaste (data not shown). These consisted of mild sensations of discomfort occurring during the first week of use. On D15, a qualitative survey was completed by the subjects from the hybrid group to evaluate the properties of the studied toothbrush subjectively. This confirmed the popularity of the toothbrush and its efficacy on hard-toclean areas.
Statistical Analysis All data were evaluated with the Shapiro-Wilk normality test. Results were plotted as mean ± standard error of the mean and statistical analyses were performed using an ANOVA test (SigmaStat, 356 SPSS Inc. , Chicago, IL, USA) or a Mann-Whitney non-parametric test, if the data did not pass the normality test. A p-value of <0. 05 was considered to be significant.
Cell Osteodifferentiation Osteodifferentiation during the 2 first weeks of incubation in osteogenic medium were assessed by ALP quantification with a colorimetric assay. After 4 days, optical density results obtained from the 3 groups did not show any difference (p = 0,180) (OD Ta = 0. 11, OD RTi = 0. 09, OD STi = 0. 10). After 8 days, some differences could be observed, with a higher osteodifferentiation into Ti surfaces, but without statistical significance (p = 0. 140) (OD Ta = 0. 26, OD RTi = 0. 3, OD STi = 0. 39). After 12 days differences increased even more, showing that osteodifferentiation was statistically higher on both Ti surfaces compared to tantalum surface (OD Ta = 0. 37, OD RTi = 0. 69, OD STi = 0. 55; p < 0,001). All data were normally distributed, and analyzed with ANOVA test.
SEM and EDX After 3 weeks of culture in osteogenic medium, samples were observed with SEM, and the following atomic elements were quantified (in percentage) with EDX spectroscopy: carbon, oxygen, phosphorus, calcium, titanium, tantalum. The quantification of these elements allowed to consider the Ca/P ratio, in order to give insight of the mineral structures. Indeed, the Ca/P ratio and Ca/O ratio can be used as reference to define the various Calcium-Phosphate crystals found in biological systems (Table  1 ). In our experiments, the high amount of oxygen detected from cellular structures did not allow to use the Ca/O ratio, as most O was coming from the cells covering the samples. The observed percentages varied according to the location, leading to data distribution that did not follow normal distribution.
Collagen Immunostaining To assess osteogenic differentiation, we quantified by immunostaining the production of type I collagen, which is found in mature extracellular matrix. After 21 days of culture, DPSC differentiation was determined for each sample by the expression of type I collagen, after immunostaining with anti-collagen antibodies, making it possible to determine the percentage of the surface covered by mature collagen. Cell nuclei were counterstained in blue. We considered the whole surface covered by collagen for analysis, expressing it as percentage of the total surface (Figure  3 ). All data were normally distributed, and a comparison was performed with ANOVA test. Overlaid images showed the RTi samples almost completely covered by collagen with a high quantity of nucleus. The analysis of surface coverage revealed that 79.
Calcium Deposits Quantification After 21 days of osteodifferentiation, calcium deposits were stained with Red Alizarin. Samples could not be observed directly under optic microscope due to their opacity. Analyses were conducted indirectly by quantifying red alizarin release after treatment with cetylpyridinium chloride. Calcium concentrations were determined by measuring absorbance at 562 nm with a plate reader. Thus, we did not quantify only calcium visible in focal plan, as for collagen immunostaining, but the whole amount of calcium on the surface and inside the pores. Data were normally distributed. Results showed a significantly higher quantity of calcium on Ta discs compared to Ti specimens with ANOVA test (OD RTi = 0. 69 ± 0. 11, OD STi = 0. 88 ± 0. 21, OD Ta = 2. 8 ± 0. 22, p < 0. 01).
Cephalometric analysis All lateral cephalograms were digitized and a customized cephalometric analysis was provided by a specific cephalometric software (Viewbox, version 3. 0, dHAL Software, Kifissia, Greece). Fourteen variables (3 angular and 11 linear) were assessed for each tracing, according to a modified Pancherz`s cephalometric analysis (Fig. 2 )  [23]. The established enlargement factor for all cephalograms was standardized to a magnification factor of 0%. All lateral cephalograms were traced initially by the same operator (J. M. ) and were checked by a second operator (L. F. ) to verify anatomical outlines, landmark placement, and superimposition. Both operators were blinded with regard to the origin of the headfilms and the group to which the patient belonged. Any disagreements were resolved to the satisfaction of both operators.
Statistics Statistical analysis was performed thanks to SPSS (SSPS. inc, IBM, Chicago, IL, USA). Standard deviation was calculated, and a T test was performed to compare the groups in terms of BMD, BV/TV, TbSp and TbN. Pearson's correlation coefficient was used to allow the comparison between histologic findings and microCT: Perry's score and BMD, Perry's score and BV/TV, Perry's score and TbN, Perry's score and TbSp.