Traditional Diagnoses Research Articles

An artificial intelligence-assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions.

The present study created an artificial intelligence (AI)-automated diagnostics system for uterine cervical lesions and assessed the performance of these images for AI diagnostic imaging of pathological cervical lesions. A total of 463 colposcopic images were analyzed. The traditional colposcopy diagnoses were compared to those obtained by AI image diagnosis. Next, 100 images were presented to a panel of 32 gynecologists who independently examined each image in a blinded fashion and diagnosed them for four categories of tumors. Then, the 32 gynecologists revisited their diagnosis for each image after being informed of the AI diagnosis. The present study assessed any changes in physician diagnosis and the accuracy of AI-image-assisted diagnosis (AISD). The accuracy of AI was 57.8% for normal, 35.4% for cervical intraepithelial neoplasia (CIN)1, 40.5% for CIN2-3 and 44.2% for invasive cancer. The accuracy of gynecologist diagnoses from cervical pathological images, before knowing the AI image diagnosis, was 54.4% for CIN2-3 and 38.9% for invasive cancer. After learning of the AISD, their accuracy improved to 58.0% for CIN2-3 and 48.5% for invasive cancer. AI-assisted image diagnosis was able to improve gynecologist diagnosis accuracy significantly (P<0.01) for invasive cancer and tended to improve their accuracy for CIN2-3 (P=0.14).

Anterior-posterior axis of hippocampal subfields across psychoses: A B-SNIP study

The hippocampus (HP) is affected across psychoses, including schizophrenia (SZ), bipolar type 1 (BDP) and schizoaffective (SAD) disorders. We examined HP subfield volumetric abnormalities along the anterior-posterior (ventral-dorsal) axis of the HP in psychosis probands, defined by traditional (DSM) diagnoses and biologically defined subtypes (biotypes, based on cognition and electrophysiology). We hypothesized that biotypes would be better discriminated by HP longitudinal axis subfields abnormalities than DSM.

S913 A Network Medicine Approach to Discover Common Novel Drug Target Mechanisms Across Autoimmune Diseases

Introduction: The map of human disease biology called the human interactome (HI) is a map of experimentally determined protein-protein interactions. Understanding the topology and dynamics of patient molecular data through analyses of the HI can reveal proteins and biological processes underlying disease development and progression. Such studies have enabled discovery and reprioritization of drug targets, insights into drug-drug interactions and identification of biomarkers for patient stratification. Application of this technology to autoimmune diseases (ADs) is particularly useful. An estimated 25-35% of patients with an ADs may develop one or more additional ADs. This points to a molecular background of immune system disfunction that could be targeted therapeutically to ameliorate a range of ADs. Methods: A network biology platform was used to generate disease modules, which defined the HI network neighborhood and proteins relevant to the underlying biology of ten diseases. Each protein in the HI was then ranked based on its network-based similarity and connectivity to the disease module to generate a list of proteins with high relevance to disease and were considered candidate protein targets for therapeutic development. Top-ranked proteins in each disease were enriched for protein targets of approved therapies. Results: The pairwise separation of 10 disease modules on the HI is illustrated in Figure 1. Among the autoimmune diseases studied, ulcerative colitis and Crohn’s disease displayed the lowest network separation. Rheumatoid arthritis was closest to multiple sclerosis and psoriatic arthritis. Disease pairs with small separation on the network are expected to have higher biological similarity in terms of common symptoms, comorbidities, and treatment opportunities. This prediction is supported by our discovery of numerous candidate drug targets that were common across all the autoimmune diseases studied. In contrast, proteins that could be targeted to influence neurological conditions such as Parkinson’s disease, Alzheimer’s disease and amyotrophic lateral sclerosis displayed a minimal network relationship to those of the autoimmune diseases. Conclusion: The network relationship between disease-related proteins of seven autoimmune diseases suggested the existence of one or more molecular mechanisms underlying human autoimmunity that traverse traditional medical disease diagnoses. Future work may delineate molecular pathways and patterns that manifest in different autoimmune disease phenotypes.Figure 1.: Disease-disease matrix heatmap showing pairwise separation on the human interactome. Red indicates small separation, reflecting similar biology and treatment opportunity. Blue indicates large separation, reflecting different biology and treatment opportunity. The heatmap suggests similar candidate targets for autoimmune diseases. This was confirmed by our target discovery and cross-comparison. CD = Crohn’s disease, UC = ulcerative colitis, RA = rheumatoid arthritis, MS = multiple sclerosis, PsA = psoriatic arthritis, SLE = systemic lupus erythematosus, PsO = psoriasis, PD = Parkinson’s disease, AD = Alzheimer’s disease, ALS = amyotrophic lateral sclerosis.

Self-reported perceptual aberrations in psychosis map to event-related potentials and semantic appraisals of objects.

Psychotic disorders have been associated with visual deficits and deviant semantic processing, making it unclear whether object detection abnormalities in psychosis originate from low-level or higher-order visual processes. The current study investigated how high-level visual processing is affected in psychosis by presenting object stimuli with equivalent low-level visual features. Outpatients with affective and nonaffective psychotic disorders, first-degree biological relatives, and psychiatrically unaffected individuals (N = 130) completed the Fragmented Ambiguous Object Task (FAOT) to assess recognition of objects in ambiguous stimuli. During the task, we recorded electroencephalography, quantified event-related potential (ERP) components (P1, N1, negative closure [NCL], N400), and derived four spatiotemporal event-related potential factors using principal components analysis (PCA). In addition to traditional diagnoses, psychosis was characterized using a dimensional measure of individual differences in self-reported sensory experiences (perceptual absorption) calculated from scales that tap the psychotic domain of the hierarchical structure of psychopathology. Rates of detecting objects within fragmented stimuli failed to differ across diagnostic groups or significantly predict perceptual absorption (p = .057). PCA factors that reflected smaller N1 and larger NCL amplitudes were associated with detecting objects. Exploratory analyses revealed that higher perceptual absorption was associated with reductions in the N400 and a late positive PCA factor. Although early and midlatency brain responses modulate during object detection, late brain responses tied to semantic appraisal of objects are related to perceptual aberrations often reported by individuals with severe psychopathology. Dimensional measures of personality appear sensitive to variation in biological systems relevant to psychotic symptomatology and object perception. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A Diagnosis and Biotype Comparison Across the Psychosis Spectrum: Investigating Volume and Shape Amygdala-Hippocampal Differences from the B-SNIP Study.

Brain-based Biotypes for psychotic disorders have been developed as part of the B-SNIP consortium to create neurobiologically distinct subgroups within idiopathic psychosis, independent from traditional phenomenological diagnostic methods. In the current study, we aimed to validate the Biotype model by assessing differences in volume and shape of the amygdala and hippocampus contrasting traditional clinical diagnoses with Biotype classification. A total of 811 participants from 6 sites were included: probands with schizophrenia (n = 199), schizoaffective disorder (n = 122), psychotic bipolar disorder with psychosis (n = 160), and healthy controls (n = 330). Biotype classification, previously developed using cognitive and electrophysiological data and K-means clustering, was used to categorize psychosis probands into 3 Biotypes, with Biotype-1 (B-1) showing reduced neural salience and severe cognitive impairment. MAGeT-Brain segmentation was used to determine amygdala and hippocampal volumetric data and shape deformations. When using Biotype classification, B-1 showed the strongest reductions in amygdala-hippocampal volume and the most widespread shape abnormalities. Using clinical diagnosis, probands with schizophrenia and schizoaffective disorder showed the most significant reductions of amygdala and hippocampal volumes and the most abnormal hippocampal shape compared with healthy controls. Biotype classification provided the strongest neuroanatomical differences compared with conventional DSM diagnoses, with the best discrimination seen using bilateral amygdala and right hippocampal volumes in B-1. These findings characterize amygdala and hippocampal volumetric and shape abnormalities across the psychosis spectrum. Grouping individuals by Biotype showed greater between-group discrimination, suggesting a promising approach and a favorable target for characterizing biological heterogeneity across the psychosis spectrum.

Relationship between transdiagnostic dimensions of psychopathology and traumatic brain injury (TBI): A TRACK-TBI study.

Neuropsychiatric symptoms are common, comorbid, and often disabling for patients with traumatic brain injury (TBI). Identifying transdiagnostic symptom dimensions post-TBI may help overcome limitations of traditional psychiatric diagnoses and advance treatment development. We characterized the dimensional structure of neuropsychiatric symptoms at 2-weeks postinjury in n = 1,732 TBI patients and n = 238 orthopedic-injured trauma controls (OTC) from the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study. Symptoms were reported on the Brief Symptom Inventory-18, Patient Health Questionnaire-9 Depression checklist, PTSD Checklist for DSM-5, PROMIS Pain Intensity scale, and Insomnia Severity Index. We established a novel factor model of neuropsychiatric symptoms and evaluated how 3 TBI severity strata and OTC patients differed in symptom severity. The final factor model had 6 first-order factors subsumed by 2 second-order factors: Internalizing (encompassing Depression, Anxiety, and Fear) and Somatic symptoms (Sleep, Physical, Pain). Somatic symptoms fit better as a correlated factor of (vs. a lower-order factor within) Internalizing. All symptom dimensions except for Pain were more severe in 1 or more TBI subgroups, as compared to the OTC group. Milder brain injury was generally associated with more severe symptoms, whereas more general injury severity (higher level of care, e.g., emergency department, intensive care unit) was associated with more pain. The findings indicate a broad factor resembling the internalizing factor of general psychopathology in traumatically injured patients, alongside a distinct somatic symptom factor. Brain injury, especially milder brain injury, may exacerbate liabilities toward these symptoms. These neuropsychiatric dimensions may help advance more precision medicine research for TBI. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Assessing Criterion A in adolescents using the Semistructured Interview for Personality Functioning DSM–5.

Accumulating evidence supports the reliability and validity of the diagnosis of personality disorders (PDs) in adolescents, but whether the current Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), criteria are optimal to capture and help detect emerging PDs in this age-group remains controversial. The Level of Personality Functioning Scale (LPFS), included in the alternative model for personality disorders, may provide a more developmentally sensitive way to identify impaired personality features in young people. This study investigates the feasibility of the LPFS in adolescents by examining the psychometric properties of the Semistructured Interview for Personality Functioning DSM-5 in a clinical sample of referred adolescents (N = 84) and in a community sample (N = 12). Additionally, referred adolescents completed self-report questionnaires pertaining to symptom severity, personality functioning, and personality traits. In general, good interrater reliability and internal consistency were observed, and the associations with external variables largely followed theoretical prediction. Interestingly, and in contrast to data on adults, we found no significant associations between the LPFS scores on the one hand and traditional DSM-5 PD diagnoses in the clinical sample on the other (except for borderline PD criteria). In discussing these findings, we argue that the assessment of personality functioning may be better suited for detecting personality pathology in adolescence than the traditional Section II criteria. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A Hierarchical Taxonomy of Psychopathology (HiTOP) Primer for Mental Health Researchers

Mental health research is at an important crossroads as the field seeks more reliable and valid phenotypes to study. Dimensional approaches to quantifying mental illness operate outside the confines of traditional categorical diagnoses, and they are gaining traction as a way to advance research on the causes and consequences of mental illness. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a leading dimensional research paradigm that synthesizes decades of data on the major dimensions of psychological disorders. In this article, we demonstrate how to use the HiTOP model to formulate and test research questions through a series of tutorials. To boost accessibility, data and annotated code for each tutorial are included at OSF ( https://osf.io/8myzw ). After presenting the tutorials, we outline how investigators can use these ideas and tools to generate new insights in their own substantive research programs.

Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): II. Externalizing superspectrum.

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical effort to address limitations of traditional mental disorder diagnoses. These include arbitrary boundaries between disorder and normality, disorder co-occurrence in the modal case, heterogeneity of presentation within dis-orders, and instability of diagnosis within patients. This paper reviews the evidence on the validity and utility of the disinhibited externalizing and antagonistic externalizing spectra of HiTOP, which together constitute a broad externalizing superspectrum. These spectra are composed of elements subsumed within a variety of mental disorders described in recent DSM nosologies, including most notably substance use disorders and "Cluster B" personality disorders. The externalizing superspectrum ranges from normative levels of impulse control and self-assertion, to maladaptive disinhibition and antagonism, to extensive polysubstance involvement and personality psychopathology. A rich literature supports the validity of the externalizing superspectrum, and the disinhibited and antagonistic spectra. This evidence encompasses common genetic influences, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, and treatment response. The structure of these validators mirrors the structure of the phenotypic externalizing superspectrum, with some correlates more specific to disinhibited or antagonistic spectra, and others relevant to the entire externalizing superspectrum, underlining the hierarchical structure of the domain. Compared with traditional diagnostic categories, the externalizing superspectrum conceptualization shows improved utility, reliability, explanatory capacity, and clinical applicability. The externalizing superspectrum is one aspect of the general approach to psychopathology offered by HiTOP and can make diagnostic classification more useful in both research and the clinic.

Predicting Long-Term Outcomes in First-Admission Psychosis: Does the Hierarchical Taxonomy of Psychopathology Aid DSM in Prognostication?

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical, dimensional model of psychological symptoms and functioning. Its goals are to augment the use and address the limitations of traditional diagnoses, such as arbitrary thresholds of severity, within-disorder heterogeneity, and low reliability. HiTOP has made inroads to addressing these problems, but its prognostic validity is uncertain. The present study sought to test the prediction of long-term outcomes in psychotic disorders was improved when the HiTOP dimensional approach was considered along with traditional (ie, DSM) diagnoses. We analyzed data from the Suffolk County Mental Health Project (N = 316), an epidemiologic study of a first-admission psychosis cohort followed for 20 years. We compared 5 diagnostic groups (schizophrenia/schizoaffective, bipolar disorder with psychosis, major depressive disorder with psychosis, substance-induced psychosis, and other psychoses) and 5 dimensions derived from the HiTOP thought disorder spectrum (reality distortion, disorganization, inexpressivity, avolition, and functional impairment). Both nosologies predicted a significant amount of variance in most outcomes. However, except for cognitive functioning, HiTOP showed consistently greater predictive power across outcomes-it explained 1.7-fold more variance than diagnoses in psychiatric and physical health outcomes, 2.1-fold more variance in community functioning, and 3.4-fold more variance in neural responses. Even when controlling for diagnosis, HiTOP dimensions incrementally predicted almost all outcomes. These findings support a shift away from the exclusive use of categorical diagnoses and toward the incorporation of HiTOP dimensions for better prognostication and linkage with neurobiology.

Dermatoscopic image melanoma recognition based on CFLDnet fusion network

As a common skin disease, malignant melanoma has attracted great attention in dermatology with high morbidity and mortality. Traditional medical diagnoses rely on clinical experience and have a certain subjective bias. Aiming at the problem of similarity between classes of melanoma dermoscopic images and the imbalance of lesion data set, we propose a CFLDnet-based dermoscopic image lesion classification model, it mainly uses the improved convolutional block attention (CBA) DenseNet algorithm to enhance beneficial features. To better integrate the attention module into DenseNet without increasing too many parameters and wasting the computing resources, we discussed three types of variant networks derived from the CFLDnet. Moreover, to balance the different categories of the data set, we also use the Sample Focal Loss (SFL) to calculate the effective sample size of the data set and smooth the focal loss function. Large numbers of comparative experiments were done based on the ISIC2018 task3 dataset, the average recognition accuracy of the CFLDnet network proposed in this paper is 86.89%, which is much higher than other similar methods (VGG16, ResNet50, InceptionV3 and DenseNet121 with cross-entropy loss function).

Clarifying the Placement of Obsessive-Compulsive Disorder in the Empirical Structure of Psychopathology

Recent work on the empirical structure of psychopathology has aimed to address some limitations that can arise from traditional categorical classification approaches. This research has focused on modeling patterns of co-occurrence among traditional diagnoses, uncovering a variety of well-validated dimensions (or spectra) of psychopathology, spanning common and uncommon mental disorders. A model integrating these empirically derived spectra (the Hierarchical Taxonomy of Psychopathology; HiTOP) has been proposed. However, the placement of obsessive-compulsive disorder (OCD) within this model remains unclear, as studies have variably found OCD to fit best as part of the Fear, Distress or Thought Disorder spectra. One reason for this may be the heterogeneity of symptoms experienced by individuals with OCD, which is lost when analysing categorical diagnoses. For example, different symptom clusters within OCD—such as washing and contamination versus obsessions and checking—may be differentially associated with different spectra in the HiTOP model. The aim of this study was to test this hypothesis. Data were collected in an anonymous online survey from community participants (n = 609), largely with elevated symptoms of mental illness, and analyzed in a factor analytic framework treating OCD as a unitary construct and as four separate symptom clusters. The results indicated that OCD and its constituent symptom clusters had significant loadings of varying strength on the Fear and Thought Disorder spectra. These findings suggest that OCD may be best characterized as cross-loading on both the Fear and Thought Disorder spectra, and highlight the importance of accounting for diagnostic heterogeneity in future research.

Using clinical expertise and empirical data in constructing networks of trauma symptoms in refugee youth

ABSTRACT Background: In recent years, many adolescents have fled their home countries due to war and human rights violations, consequently experiencing various traumatic events and putting them at risk of developing mental health problems. The symptomatology of refugee youth was shown to be multifaceted and often falling outside of traditional diagnoses. Objective: The present study aimed to investigate the symptomatology of this patient group by assessing the network structure of a wide range of symptoms. Further, we assessed clinicians’ perceptions of symptoms relations in order to evaluate the clinical validity of the empirical network. Methods: Empirical data on Post-Traumatic Stress Disorder (PTSD), depression and other trauma symptoms from N = 366 refugee youth were collected during the routine diagnostic process of an outpatient centre for refugee youth in Germany. Additionally, four clinicians of this outpatient centre were asked how they perceive symptom relations in their patients using a newly developed tool. Separate networks were constructed based on 1) empirical symptom data and 2) clinicians’ perceived symptom relations (PSR). Results: Both the network based on empirical data and the network based on clinicians’ PSR showed that symptoms of PTSD and depression related most strongly within each respective cluster (connected mainly via sleeping problems), externalizing symptoms were somewhat related to PTSD symptoms and intrusions were central. Some differences were found within the clinicians’ PSR as well as between the PSR and the empirical network. Still, the general PSR-network structure showed a moderate to good fit to the empirical data. Conclusion: Our results suggest that sleeping problems and intrusions play a central role in the symptomatology of refugee children, which has tentative implications for diagnostics and treatment. Further, externalizing symptoms might be an indicator for PTSD-symptoms. Finally, using clinicians’ PSR for network construction offered a promising possibility to gain information on symptom networks and their clinical validity.

Differing functional mechanisms underlie cognitive control deficits in psychotic spectrum disorders.

Functional underpinnings of cognitive control deficits in unbiased samples (i.e., all comers) of patients with psychotic spectrum disorders (PSD) remain actively debated. While many studies suggest hypofrontality in the lateral prefrontal cortex (PFC) and greater deficits during proactive relative to reactive control, few have examined the full hemodynamic response. Patients with PSD (n = 154) and healthy controls (n = 65) performed the AX continuous performance task (AX-CPT) during rapid (460 ms) functional neuroimaging and underwent full clinical characterization. Behavioural results indicated generalized cognitive deficits (slower and less accurate) across proactive and reactive control conditions in patients with PSD relative to healthy controls. We observed a delayed/prolonged neural response in the left dorsolateral PFC, the sensorimotor cortex and the superior parietal lobe during proactive control for patients with PSD. These proactive hemodynamic abnormalities were better explained by negative rather than by positive symptoms or by traditional diagnoses according to the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition, Text Revision (DSM-IV-TR), with subsequent simulations unequivocally demonstrating how these abnormalities could be erroneously interpreted as hypoactivation. Conversely, true hypoactivity, unassociated with clinical symptoms or DSM-IV-TR diagnoses, was observed within the ventrolateral PFC during reactive control. In spite of guidance for AX-CPT use in neuroimaging studies, one-third of patients with PSD could not perform the task above chance and were more clinically impaired. Current findings question the utility of the AX-CPT for neuroimaging-based appraisal of cognitive control across the full spectrum of patients with PSD. Previously reported lateral PFC "hypoactivity" during proactive control may be more indicative of a delayed/prolonged neural response, important for rehabilitative purposes. Negative symptoms may better explain certain behavioural and hemodynamic abnormalities in patients with PSD relative to DSM-IV-TR diagnoses.

Continental-scale patterns of hyper-cryptic diversity within the freshwater model taxon Gammarus fossarum (Crustacea, Amphipoda)

Traditional morphological diagnoses of taxonomic status remain widely used while an increasing number of studies show that one morphospecies might hide cryptic diversity, i.e. lineages with unexpectedly high molecular divergence. This hidden diversity can reach even tens of lineages, i.e. hyper cryptic diversity. Even well-studied model-organisms may exhibit overlooked cryptic diversity. Such is the case of the freshwater crustacean amphipod model taxon Gammarus fossarum. It is extensively used in both applied and basic types of research, including biodiversity assessments, ecotoxicology and evolutionary ecology. Based on COI barcodes of 4926 individuals from 498 sampling sites in 19 European countries, the present paper shows (1) hyper cryptic diversity, ranging from 84 to 152 Molecular Operational Taxonomic Units, (2) ancient diversification starting already 26 Mya in the Oligocene, and (3) high level of lineage syntopy. Even if hyper cryptic diversity was already documented in G. fossarum, the present study increases its extent fourfold, providing a first continental-scale insight into its geographical distribution and establishes several diversification hotspots, notably south-eastern and central Europe. The challenges of recording hyper cryptic diversity in the future are also discussed.

Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): I. Psychosis superspectrum.

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a scientific effort to address shortcomings of traditional mental disorder diagnoses, which suffer from arbitrary boundaries between psychopathology and normality, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. This paper synthesizes evidence on the validity and utility of the thought disorder and detachment spectra of HiTOP. These spectra are composed of symptoms and maladaptive traits currently subsumed within schizophrenia, other psychotic disorders, and schizotypal, paranoid and schizoid personality disorders. Thought disorder ranges from normal reality testing, to maladaptive trait psychoticism, to hallucinations and delusions. Detachment ranges from introversion, to maladaptive detachment, to blunted affect and avolition. Extensive evidence supports the validity of thought disorder and detachment spectra, as each spectrum reflects common genetics, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, biomarkers, and treatment response. Some of these characteristics are specific to one spectrum and others are shared, suggesting the existence of an overarching psychosis superspectrum. Further research is needed to extend this model, such as clarifying whether mania and dissociation belong to thought disorder, and explicating processes that drive development of the spectra and their subdimensions. Compared to traditional diagnoses, the thought disorder and detachment spectra demonstrated substantially improved utility: greater reliability, larger explanatory and predictive power, and higher acceptability to clinicians. Validated measures are available to implement the system in practice. The more informative, reliable and valid characterization of psychosis-related psychopathology offered by HiTOP can make diagnosis more useful for research and clinical care.

Unanticipated critical findings on echocardiography in septic patients

BackgroundEchocardiography is increasingly performed among septic patients as a routine part of evaluation and management in the intensive care unit (ICU). The rate of unanticipated critical findings (e.g., severe left or right ventricular dysfunction or pericardial tamponade) on such echocardiograms is unknown. We evaluated a retrospective cohort of septic ICU patients in whom transthoracic echocardiography was performed as a routine part of sepsis management. In addition to identifying critical findings, we defined whether each critical finding was anticipated, and whether the clinical team responded to the critical finding. The primary outcome was rate of unanticipated critical findings, which we hypothesized would occur in fewer than 5% of patients. We also performed an exploratory analysis of the association between unanticipated critical finding and mortality, controlling for severity of illness.ResultsWe studied 393 patients. Unanticipated critical findings were identified in 5% (95% CI 3–7%) of patients (n = 20). Among the 20 patients with unanticipated critical findings, a response to the unanticipated critical finding was identified in 12 (60%) patients. An unanticipated critical finding was not significantly associated with 28-day mortality when controlling for admission APACHE II (p = 0.27).ConclusionsUnanticipated critical findings on echocardiograms in septic ICU patients are uncommon. The potential therapeutic relevance of echocardiography to sepsis is more likely related to hemodynamic management than to traditional cardiac diagnoses. Research studies that employ blinded echocardiograms in septic patients may anticipate unblinding for critical findings approximately 1 in every 20 echocardiograms.

Neurobiology and the Hierarchical Taxonomy ofPsychopathology: progress toward ontogenetically informed and clinically usefulnosology

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical structural model of psychological symptoms formulated to improve the reliability and validity of clinical assessment. Neurobiology can inform assessments of early risk and intervention strategies, and the HiTOP model has greater potential to interface with neurobiological measures than traditional categorical diagnoses given its enhanced reliability. However, one complication is that observed biological correlates of clinical symptoms can reflect various factors, ranging from dispositional risk to consequences of psychopathology. In this paper, we argue that the HiTOP model provides an optimized framework for conducting research on the biological correlates of psychopathology from an ontogenetic perspective that distinguishes among indicators of liability, current symptoms, and consequences of illness. Through this approach, neurobiological research can contribute more effectively to identifying individuals at high dispositional risk, indexing treatment-related gains, and monitoring the consequences of mental illness, consistent with the aims of the HiTOP framework.

Factor analyses of multidimensional symptoms in a large group of patients with major depressive disorder, bipolar disorder, schizoaffective disorder and schizophrenia

BackgroundThere is an ongoing discussion about which neurobiological correlates or symptoms separate the major psychoses (i.e. Major Depressive Disorder MDD, Bipolar Disorder BD, and Schizophrenia SZ). Psychopathological factor analyses within one of these disorders have resulted in models including one to five factors. Factor analyses across the major psychoses using a comprehensive set of psychopathological scales in the same patients are lacking. It is further unclear, whether hierarchical or unitarian models better summarize phenomena. MethodPatients (n = 1182) who met DSM-IV criteria for MDD, BD, SZ or schizoaffective disorder were assessed with the SANS, SAPS, HAMA, HAM-D, and YMRS. The sample was split into two and analyzed using explorative and confirmatory factor analyses to extract psychopathological factors independent of diagnosis. ResultsIn the exploratory analysis of sample 1 (n = 593) we found 5 factors. The confirmatory analysis using sample 2 (n = 589) confirmed the 5-factor model (χ2 = 1287.842, df = 571, p < .0001: CFI = 0.932; RMSEA = 0.033). The 5-factors were depression, negative syndrome, positive formal thought disorder, paranoid-hallucinatory syndrome, and increased appetite. Increased appetite was not related to medication. None of the factors was specific for one diagnosis. Second order factor analysis revealed two higher order factors: negative/affective (I) and positive symptoms (II). ConclusionThis is the first study delineating psychopathological factors in a large group of patients across the spectrum of affective and psychotic disorders. In future neurobiological studies, we should consider transdiagnostic syndromes besides the traditional diagnoses.

A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories

The breast ultrasound is not only one of major devices for breast tissue imaging, but also one of important methods in breast tumor screening. It is non-radiative, non-invasive, harmless, simple, and low cost screening. The American College of Radiology (ACR) proposed the Breast Imaging Reporting and Data System (BI-RADS) to evaluate far more breast lesion severities compared to traditional diagnoses according to five-criterion categories of masses composition described as follows: shape, orientation, margin, echo pattern, and posterior features. However, there exist some problems, such as intensity differences and different resolutions in image acquisition among different types of ultrasound imaging modalities so that clinicians cannot always identify accurately the BI-RADS categories or disease severities. To this end, this article adopted three different brands of ultrasound scanners to fetch breast images for our experimental samples. The breast lesion was detected on the original image using preprocessing, image segmentation, etc. The breast tumor’s severity was evaluated on the features of the breast lesion via our proposed classifiers according to the BI-RADS standard rather than traditional assessment on the severity; i.e., merely using benign or malignant. In this work, we mainly focused on the BI-RADS categories 2–5 after the stage of segmentation as a result of the clinical practice. Moreover, several features related to lesion severities based on the selected BI-RADS categories were introduced into three machine learning classifiers, including a Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN) combined with feature selection to develop a multi-class assessment of breast tumor severity based on BI-RADS. Experimental results show that the proposed CAD system based on BI-RADS can obtain the identification accuracies with SVM, RF, and CNN reaching 80.00%, 77.78%, and 85.42%, respectively. We also validated the performance and adaptability of the classification using different ultrasound scanners. Results also indicate that the evaluations of F-score based on CNN can obtain measures higher than 75% (i.e., prominent adaptability) when samples were tested on various BI-RADS categories.