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Abstract Objective Panoramic radiographs (PRs) provide a comprehensive view of the oral and maxillofacial region and are used routinely to assess dental and osseous pathologies. Artificial intelligence (AI) can be used to improve the diagnostic accuracy of PRs compared to bitewings and periapical radiographs. This study aimed to evaluate the advantages and challenges of using publicly available datasets in dental AI research, focusing on solving the novel task of predicting tooth segmentations, FDI numbers, and tooth diagnoses, simultaneously. Materials and methods Datasets from the OdontoAI platform (tooth instance segmentations) and the DENTEX challenge (tooth bounding boxes with associated diagnoses) were combined to develop a two-stage AI model. The first stage implemented tooth instance segmentation with FDI numbering and extracted regions of interest around each tooth segmentation, whereafter the second stage implemented multi-label classification to detect dental caries, impacted teeth, and periapical lesions in PRs. The performance of the automated tooth segmentation algorithm was evaluated using a free-response receiver-operating-characteristics (FROC) curve and mean average precision (mAP) metrics. The diagnostic accuracy of detection and classification of dental pathology was evaluated with ROC curves and F1 and AUC metrics. Results The two-stage AI model achieved high accuracy in tooth segmentations with a FROC score of 0.988 and a mAP of 0.848. High accuracy was also achieved in the diagnostic classification of impacted teeth (F1 = 0.901, AUC = 0.996), whereas moderate accuracy was achieved in the diagnostic classification of deep caries (F1 = 0.683, AUC = 0.960), early caries (F1 = 0.662, AUC = 0.881), and periapical lesions (F1 = 0.603, AUC = 0.974). The model’s performance correlated positively with the quality of annotations in the used public datasets. Selected samples from the DENTEX dataset revealed cases of missing (false-negative) and incorrect (false-positive) diagnoses, which negatively influenced the performance of the AI model. Conclusions The use and pooling of public datasets in dental AI research can significantly accelerate the development of new AI models and enable fast exploration of novel tasks. However, standardized quality assurance is essential before using the datasets to ensure reliable outcomes and limit potential biases.

Abstract Background The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. Methods Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. Results A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. Conclusions Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Autism spectrum disorder (ASD) is diagnosed on the basis of speech and communication differences, amongst other symptoms. Since conversations are essential for building connections with others, it is important to understand the exact nature of differences between autistic and non-autistic verbal behaviour and evaluate the potential of these differences for diagnostics. In this study, we recorded dyadic conversations and used automated extraction of speech and interactional turn-taking features of 54 non-autistic and 26 autistic participants. The extracted speech and turn-taking parameters showed high potential as a diagnostic marker. A linear support vector machine was able to predict the dyad type with 76.2% balanced accuracy (sensitivity: 73.8%, specificity: 78.6%), suggesting that digitally assisted diagnostics could significantly enhance the current clinical diagnostic process due to their objectivity and scalability. In group comparisons on the individual and dyadic level, we found that autistic interaction partners talked slower and in a more monotonous manner than non-autistic interaction partners and that mixed dyads consisting of an autistic and a non-autistic participant had increased periods of silence, and the intensity, i.e. loudness, of their speech was more synchronous.

There is scarce evidence to demonstrate the pattern of antibiotic use in children in China. We aimed to describe antibiotic prescribing practices among children in primary healthcare institutions (PHIs) in China. We described outpatient antibiotic prescriptions for children in PHIs from January 2017 to December 2019 at both the national and diagnostic levels, utilizing the antibiotic prescribing rate (APR), multi-antibiotic prescribing rate (MAPR), and broad-spectrum prescribing rate (BAPR). Generalized estimating equations were adopted to analyze the factors associated with antibiotic use. Among the total 155,262.2 weighted prescriptions for children, the APR, MAPR, and BAPR were 43.5%, 9.9%, and 84.8%. At the national level, J01DC second-generation cephalosporins were the most prescribed antibiotic category (21.0%, N = 15,313.0), followed by J01DD third-generation cephalosporins (17.4%, N = 12,695.8). Watch group antibiotics accounted for 55.0% of the total antibiotic prescriptions (N = 52,056.3). At the diagnostic level, respiratory tract infections accounted for 67.4% of antibiotic prescriptions, among which prescriptions with diagnoses classified as potentially bacterial RTIs occupied the highest APR (55.0%). For each diagnostic category, the MAPR and BAPR varied. Age, region, and diagnostic categories were associated with antibiotic use. Concerns were raised regarding the appropriateness of antibiotic use, especially for broad-spectrum antibiotics.

Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results.

Introduction Attention Deficit / Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition characterized by inattention, motor hyperactivity and impulsivity. ADHD cognitive and behavioral presentation is characterized by a high heterogeneity (APA, 2013). Indeed, a complex diagnostic process, that considers several validated tools, is, to date, necessary. Objectives The main aim is to develop supervised machine learning (ML) algorithms that could be used to support the diagnostic process for ADHD, by identifying the most relevant features in discriminating between the presence or absence of the ADHD diagnosis in children. Methods We analyzed data from 342 children (Mean age: 8y 8m ± 1y; 61 F) referred for possible ADHD symptomatology. Assessments were performed by an expert clinician and through questionnaires: Social Responsiveness Scale (SRS), Child Behavior Checklist (CBCL), Conners Rating Scale for Parents (CPRS) and for Teachers (CTRS). Data were analyzed using a decision tree classifier and random forest algorithms. Results The decision tree model performed an accuracy of 0.71. The random forest model that was identified as the best tested, performed an accuracy of 0.77 (Figure 1) and it identified as most informative parent- and teacher-rated DSM-oriented ADHD symptoms (Figure 2). Figure 1: Random forest confusion matrix and statistics. Figure 2: Ranking of variables importance. Conclusions A random forest classifier could represent an effective algorithm to support the identification of ADHD children and to simplify the diagnostic process as an initial step. The use of supervised machine learning algorithms could be useful in helping the diagnostic process, highlighting the importance of a personalized medicine approach. Disclosure No significant relationships.

Purpose: This study was conducted in order to compare the diagnostic classification of Bruch's membrane opening-minimum rim width (BMO-MRW) and RNFL thickness in normal myopic subjects by using optical coherence tomography (OCT).Methods: This cross-sectional study involved 75 healthy myopic subjects [spherical equivalent (SE) ≤ −0.5D] from April 2019 to January 2020. One eye of each subject was randomly selected for examination. BMO-MRW and peripapillary RNFL thickness were measured by spectral-domain OCT (Spectralis, Heidelberg Engineering GmbH, Heidelberg, Germany). All the subjects were divided into three groups: low myopic group (SE > −3D), moderate myopic group (−6D < SE ≤ −3D), and high myopic group (SE ≤ −6D). A nonparametric test was used to analyze the difference among groups. Linear regression was used to analyze the relationship between BMO-MRW/RNFL thickness and axial length/spherical equivalent. McNemar test was used to compare the diagnostic classification between BMO-MRW and RNFL thickness.Results: The RNFL thickness classified a significantly higher percentage of eyes as outside normal limits/borderline in at least 1 quadrant (BMO-MRW, 4%; RNFL thickness, 34.67%; p < 0.01). There was no significant correlation between BMO-MRW/RNFL thickness and AL/SE. The low myopia (SE > −3D) had a significantly lower percentage of eyes classified as outside normal limits/borderline in at least 1 quadrant than the moderate myopia (−6D < SE ≤ −3D) and high myopia (SE ≤ −6D) (low myopia, 12.5%; moderate/high myopia, 42.42%/50%; p < 0.05).Conclusion: BMO-MRW had a lower percentage of eyes classified as outside normal limits/borderline in at least 1 quadrant than RNFL thickness in normal myopic subjects. When referring to the diagnostic classification of RNFL thickness in myopic subjects, caution should be exercised in interpreting positive results. Further studies are needed to compare the diagnostic accuracy of these two measurements in myopic glaucoma patients.

Diagnostic criteria for complex medical conditions caused by a multitude of both genetic and environmental factors should be descriptive and avoid any attribution of causality. Furthermore, the wording used to describe a disorder should be evidence-based and avoid stigmatization of the affected individuals. Both terminology and categorizations should be readily comprehensible for healthcare professionals and guide clinical decision making. Uncertainties with respect to diagnostic issues and their implications may be addressed to direct future clinical research. In this context, the European Association of the Study of Obesity (EASO) considers it an important endeavor to review the current ICD-11 Beta Draft for the definition of overweight and obesity and to propose a substantial revision. We aim to provide an overview of the key issues that we deem relevant for the discussion of the diagnostic criteria. We first discuss the current ICD-10 criteria and those proposed in the ICD 11 Beta Draft. We conclude with our own proposal for diagnostic criteria, which we believe will improve the assessment of patients with obesity in a clinically meaningful way.

Abstract Introduction We established a method for diagnostic harmonization across multiple studies of preclinical Alzheimer's disease and validated the method by examining its relationship with clinical status and cognition. Methods Cognitive and clinical data were used from five studies (N = 1746). Consensus diagnoses established in each study used criteria to identify progressors from normal cognition to mild cognitive impairment. Correspondence was evaluated between these consensus diagnoses and three algorithmic classifications based on (1) objective cognitive impairment in 2+ tests only; (2) a Clinical Dementia Rating (CDR) of ≥0.5 only; and (3) both. Associations between baseline cognitive performance and cognitive change were each tested in relation to progression to algorithm‐based classifications. Results In each study, an algorithmic classification based on both cognitive testing cutoff scores and a CDR ≥0.5 provided optimal balance of sensitivity and specificity (areas under the curve: 0.85–0.95). Over an average 6.6 years of follow‐up (up to 28 years), N = 186 initially cognitively normal participants aged on average 64 years at baseline progressed (incidence rate: 15.3 people/1000 person‐years). Baseline cognitive scores and cognitive change were associated with future diagnostic status using this algorithmic classification. Discussion Both cognitive tests and CDR ratings can be combined across multiple studies to obtain a reliable algorithmic classification with high specificity and sensitivity. This approach may be applicable to large cohort studies and to clinical trials focused on preclinical Alzheimer's disease because it provides an alternative to implementation of a time‐consuming adjudication panel.

Objective: The Hierarchical Taxonomy of Psychopathology (HiTOP) posits that psychopathology is hierarchically structured. For personality disorder (PD) traits, there are five spectra: internalizing, thought disorder, disinhibited externalizing, antagonistic externalizing, and detachment. Empirical findings suggest a sixth group, compulsivity. In this research, we tried to recover the five HiTOP spectra, plus compulsivity, specifically for PD traits. Methods: The sample was composed of 4,868 Brazilians (54.9% women, age ranging from 18 to 70; mean = 25.7; SD = 9.64). All participants answered the Dimensional Clinical Personality Inventory 2 (IDCP-2), a self-report inventory for adults, developed in Brazil, for assessment of pathological personality traits. Results: Parallel analysis yielded up to nine factors. On exploratory structural equation modeling (E-SEM), the balance between interpretability and fit index suggested the six-factor solution as the best solution. The fit indexes for the confirmatory factor analysis were slightly less adjusted in comparison to the empirical model. Conclusion: The hypothesis was confirmed, as we did find the groups proposed at the spectrum level of the HiTOP. We also found a compulsivity factor, encompassing the main traits from the conscientiousness dimension of IDCP-2, which is related to obsessive-compulsive PD. Finding the six groupings of traits in the HiTOP model contributes to the validity of this model, and confirms the existence of proposed spectra.

The Diagnostic and Statistical Manual of Mental Disorders ( DSM ) has provided a standard language that physicians, scientists, and public health specialists use to communicate about mental disorders. This guideline was revised for the first time in 1986 and its last edition (fifth edition) was published in 2013. The most important modifications in the DSM-5 include the change in font, reexamination of the definition of psychological disorder, the order of categories, considerations for lifetime growth and developmental issues, categorization of disorders with greater emphasis on neuroscience and less emphasis on symptom emergence, differentiation between main diagnosis and the reason for referral, determination of temporary or absolute diagnosis, new categories of other specified and unspecified disorders, tendency toward dimensional evaluation, decrease in diagnosis-centered system, emphasis on cultural issues, emphasis on gender issues, and reexamination of diagnosis criteria. The aim of the present article was a critical, applied, and integrated investigation into these modifications. The new version received the most criticisms regarding extreme medicalization of normal issues. The main controversies regarding this version are related to the definition of some disorders, diagnostic inflation, and inappropriate impact of drug companies. In the final section of the article, the future perspectives of psychological disorder categories are explored. It seems that the Research Domain Criteria (RDoC) Project will play an important role in psychological disorder categorization in the future.

Frontal-subcortical circuit dysfunction is commonly associated with apathy, a neuropsychiatric sequelae of human immunodeficiency virus type-1 (HIV-1). Behavioral and neurochemical indices of apathy in the nucleus accumbens (NAc), a key brain region involved in frontal-subcortical circuitry, are influenced by the factor of biological sex. Despite evidence of sex differences in HIV-1, the effect of biological sex on medium spiny neurons (MSNs), which are central integrators of frontal-subcortical input, has not been systematically evaluated. In the present study, a DiOlistic labeling technique was used to investigate the role of long-term HIV-1 viral protein exposure, the factor of biological sex, and their possible interaction, on synaptic dysfunction in MSNs of the NAc in the HIV-1 transgenic (Tg) rat. HIV-1 Tg rats, independent of biological sex, displayed profound alterations in synaptic connectivity, evidenced by a prominent shift in the distribution of dendritic spines. Female HIV-1 Tg rats, but not male HIV-1 Tg rats, exhibited alterations in dendritic branching and neuronal arbor complexity relative to control animals, supporting an alteration in glutamate neurotransmission. Morphologically, HIV-1 Tg male, but not female HIV-1 Tg rats, displayed a population shift towards decreased dendritic spine volume, suggesting decreased synaptic area, relative to control animals. Synaptic dysfunction accurately identified presence of the HIV-1 transgene, dependent upon biological sex, with at least 80% accuracy (i.e., Male: 80%; Female: 90%). Collectively, these results support a primary alteration in circuit connectivity, the mechanism of which is dependent upon biological sex. Understanding the effect of biological sex on the underlying neural mechanism for HIV-1 associated apathy is vital for the development of sex-based therapeutics and cure strategies.

Internet gaming disorder (IGD) is often diagnosed on the basis of nine underlying criteria from the latest version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Here, we examined whether such symptom-based categorization could be translated into computation-based classification. Structural MRI (sMRI) and diffusion-weighted MRI (dMRI) data were acquired in 38 gamers diagnosed with IGD, 68 normal gamers diagnosed as not having IGD, and 37 healthy non-gamers. We generated 108 features of gray matter (GM) and white matter (WM) structure from the MRI data. When regularized logistic regression was applied to the 108 neuroanatomical features to select important ones for the distinction between the groups, the disordered and normal gamers were represented in terms of 43 and 21 features, respectively, in relation to the healthy non-gamers, whereas the disordered gamers were represented in terms of 11 features in relation to the normal gamers. In support vector machines (SVM) using the sparse neuroanatomical features as predictors, the disordered and normal gamers were discriminated successfully, with accuracy exceeding 98%, from the healthy non-gamers, but the classification between the disordered and normal gamers was relatively challenging. These findings suggest that pathological and non-pathological gamers as categorized with the criteria from the DSM-5 could be represented by sparse neuroanatomical features, especially in the context of discriminating those from non-gaming healthy individuals.

This article examines the history of the conceptualization of dissociative, conversion, and somatoform syndromes in relation to one another, chronicles efforts to classify these and other phenomenologically-related psychopathology in the American diagnostic system for mental disorders, and traces the subsequent divergence in opinions of dissenting sectors on classification of these disorders. This article then considers the extensive phenomenological overlap across these disorders in empirical research, and from this foundation presents a new model for the conceptualization of these disorders. The classification of disorders formerly known as hysteria and phenomenologically-related syndromes has long been contentious and unsettled. Examination of the long history of the conceptual difficulties, which remain inherent in existing classification schemes for these disorders, can help to address the continuing controversy. This review clarifies the need for a major conceptual revision of the current classification of these disorders. A new phenomenologically-based classification scheme for these disorders is proposed that is more compatible with the agnostic and atheoretical approach to diagnosis of mental disorders used by the current classification system.

BackgroundThe approach to apply multivariate pattern analyses based on neuro imaging data for outcome prediction holds out the prospect to improve therapeutic decisions in mental disorders. Patients suffering from panic disorder with agoraphobia (PD/AG) often exhibit an increased perception of bodily sensations. The purpose of this investigation was to assess whether multivariate classification applied to a functional magnetic resonance imaging (fMRI) interoception paradigm can predict individual responses to cognitive behavioral therapy (CBT) in PD/AG.MethodsThis analysis is based on pretreatment fMRI data during an interoceptive challenge from a multicenter trial of the German PANIC-NET. Patients with DSM-IV PD/AG were dichotomized as responders (n = 30) or non-responders (n = 29) based on the primary outcome (Hamilton Anxiety Scale Reduction ≥50%) after 6 weeks of CBT (2 h/week). fMRI parametric maps were used as features for response classification with linear support vector machines (SVM) with or without automated feature selection. Predictive accuracies were assessed using cross validation and permutation testing. The influence of methodological parameters and the predictive ability for specific interoception-related symptom reduction were further evaluated.ResultsSVM did not reach sufficient overall predictive accuracies (38.0–54.2%) for anxiety reduction in the primary outcome. In the exploratory analyses, better accuracies (66.7%) were achieved for predicting interoception-specific symptom relief as an alternative outcome domain. Subtle information regarding this alternative response criterion but not the primary outcome was revealed by post hoc univariate comparisons.ConclusionIn contrast to reports on other neurofunctional probes, SVM based on an interoception paradigm was not able to reliably predict individual response to CBT. Results speak against the clinical applicability of this technique.

This article provides an overview of the history of substance use and misuse and chronicles the long shared history humans have had with psychoactive substances, including alcohol. The practical and personal functions of substances and the prevailing views of society towards substance users are described for selected historical periods and within certain cultural contexts. This article portrays how the changing historical and cultural milieu influences the prevailing medical, moral, and legal conceptualizations of substance use as reflected both in popular opinion and the consensus of the scientific community and represented by the American Psychiatric Association’s (APA) Diagnostic and Statistical Manual of Mental Disorders (DSM). Finally, this article discusses the efforts to classify substance use disorders (SUDs) and associated psychopathology in the APA compendium. Controversies both lingering and resolved in the field are discussed, and implications for the future of SUD diagnoses are identified.

This article traces the history of classification systems for mental illness and then reviews the history of the American diagnostic system for mental disorders. The steps leading up to each publication of the Diagnostic and Statistical Manual (DSM) are described including leaders, timelines, pre-publication meetings, and field trials. Important changes in the purpose of the manuals are described with a focus on events leading to the manual’s third edition (DSM-III), which represented a paradigm shift in how we think about, and use, the classification system for mental illness. For the first time, DSM-III emphasized empirically-based, atheoretical and agnostic diagnostic criteria. New criticisms of the DSM-III and subsequent editions have arisen with a call for a new paradigm shift to replace diagnostic categories with continuous dimensional systems of classification, returning to etiologically-based definitions and incorporating findings from neurobiological science into systems of diagnosis. In the foreseeable future, however, psychiatric diagnosis must continue to be accomplished by taking a history and assessing the currently established criteria. This is necessary for communication about diseases and education of clinicians and scientists in medical fields, as well as advancement of research needed to further advance the diagnostic criteria of psychiatry.