Background Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). Methods N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally. Results Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82). Conclusions Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.

<b><i>Introduction:</i></b> Motor abnormalities have been shown to be a distinct component of schizophrenia symptomatology. However, objective and scalable methods for assessment of motor functioning in schizophrenia are lacking. Advancements in machine learning-based digital tools have allowed for automated and remote “digital phenotyping” of disease symptomatology. Here, we assess the performance of a computer vision-based assessment of motor functioning as a characteristic of schizophrenia using video data collected remotely through smartphones. <b><i>Methods:</i></b> Eighteen patients with schizophrenia and 9 healthy controls were asked to remotely participate in smartphone-based assessments daily for 14 days. Video recorded from the smartphone front-facing camera during these assessments was used to quantify the Euclidean distance of head movement between frames through a pretrained computer vision model. The ability of head movement measurements to distinguish between patients and healthy controls as well as their relationship to schizophrenia symptom severity as measured through traditional clinical scores was assessed. <b><i>Results:</i></b> The rate of head movement in participants with schizophrenia (1.48 mm/frame) and those without differed significantly (2.50 mm/frame; <i>p</i> = 0.01), and a logistic regression demonstrated that head movement was a significant predictor of schizophrenia diagnosis (<i>p</i> = 0.02). Linear regression between head movement and clinical scores of schizophrenia showed that head movement has a negative relationship with schizophrenia symptom severity (<i>p</i> = 0.04), primarily with negative symptoms of schizophrenia. <b><i>Conclusions:</i></b> Remote, smartphone-based assessments were able to capture meaningful visual behavior for computer vision-based objective measurement of head movement. The measurements of head movement acquired were able to accurately classify schizophrenia diagnosis and quantify symptom severity in patients with schizophrenia.

Background: Context: DKA is an important complication of undiagnosed or poorly controlled diabetes mellitus. Proper management of DKA can prevent morbidity and mortality attributable to diabetes mellitus. The aim of the research was to study the clinical profile and outcome of the children admitted with Diabetic Ketoacidosis (DKA).Methods: A descriptive retrospective study was conducted in pediatric ICU of tertiary level care hospital over three years between January 2013 and December 2015. 29 patients were diagnosed with DKA during the three-year period, the data was collected by reviewing the medical record of the patient and information with respect to personal details, clinical features, laboratory parameters, management and outcome was recorded.Results: 29 patients were diagnosed with DKA, of these 17 were males and 12 females. M:F was 1.4:1 and mean age at presentation was 11.4±4.4 yrs. DKA was the presenting manifestation of Diabetes in 48.2% patients and 51.8% were already known cases of Diabetes. Abdominal pain (62%), polyuria (58.6%), fast breathing (58.6%), vomiting (55.1%), and altered sensorium (44.8%) were common presenting symptoms of DKA. Severe ketoacidosis was noted in 48.2% and severe dehydration in 31%. Shock was observed in 27.5% patients and 20.9% had cerebral edema. Metabolic abnormalities like hponatremia, hypernatremia, hyperkalemia, hypokalemia were seen in 44.8%, 13.7%, 24.1%, 17.2% respectively. We had 1 (3.4%) mortality.Conclusions: Diabetic Ketoacidosis (DKA) is an important cause of hospital admissions and 48.2% of newly diagnosed cases presented with DKA. Infections and omission of the insulin were the most common precipitating factors. For the long- term management strategy it is important to educate of the patients and their parents regarding regular blood sugar monitoring and insulin dosing.

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Background Machine learning–based facial and vocal measurements have demonstrated relationships with schizophrenia diagnosis and severity. Demonstrating utility and validity of remote and automated assessments conducted outside of controlled experimental or clinical settings can facilitate scaling such measurement tools to aid in risk assessment and tracking of treatment response in populations that are difficult to engage. Objective This study aimed to determine the accuracy of machine learning–based facial and vocal measurements acquired through automated assessments conducted remotely through smartphones. Methods Measurements of facial and vocal characteristics including facial expressivity, vocal acoustics, and speech prevalence were assessed in 20 patients with schizophrenia over the course of 2 weeks in response to two classes of prompts previously utilized in experimental laboratory assessments: evoked prompts, where subjects are guided to produce specific facial expressions and speech; and spontaneous prompts, where subjects are presented stimuli in the form of emotionally evocative imagery and asked to freely respond. Facial and vocal measurements were assessed in relation to schizophrenia symptom severity using the Positive and Negative Syndrome Scale. Results Vocal markers including speech prevalence, vocal jitter, fundamental frequency, and vocal intensity demonstrated specificity as markers of negative symptom severity, while measurement of facial expressivity demonstrated itself as a robust marker of overall schizophrenia symptom severity. Conclusions Established facial and vocal measurements, collected remotely in schizophrenia patients via smartphones in response to automated task prompts, demonstrated accuracy as markers of schizophrenia symptom severity. Clinical implications are discussed.

Objectives: Multiple machine learning-based visual and auditory digital markers have demonstrated associations between major depressive disorder (MDD) status and severity. The current study examines if such measurements can quantify response to antidepressant treatment (ADT) with selective serotonin reuptake inhibitors (SSRIs) and serotonin–norepinephrine uptake inhibitors (SNRIs).Methods: Visual and auditory markers were acquired through an automated smartphone task that measures facial, vocal, and head movement characteristics across 4 weeks of treatment (with time points at baseline, 2 weeks, and 4 weeks) on ADT (n = 18). MDD diagnosis was confirmed using the Mini-International Neuropsychiatric Interview (MINI), and the Montgomery–Åsberg Depression Rating Scale (MADRS) was collected concordantly to assess changes in MDD severity.Results: Patient responses to ADT demonstrated clinically and statistically significant changes in the MADRS [F(2, 34) = 51.62, p &lt; 0.0001]. Additionally, patients demonstrated significant increases in multiple digital markers including facial expressivity, head movement, and amount of speech. Finally, patients demonstrated significantly decreased frequency of fear and anger facial expressions.Conclusion: Digital markers associated with MDD demonstrate validity as measures of treatment response.

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Background Multiple symptoms of suicide risk have been assessed based on visual and auditory information, including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment. Objective We aimed to examine measurements extracted from video interviews using open-source deep learning algorithms to quantify facial, vocal, and movement behaviors in relation to suicide risk severity in recently admitted patients following a suicide attempt. Methods We utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open-source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide risk with the Beck Scale for Suicide Ideation controlling for age and sex, using multiple linear regression. Results Suicide severity was associated with multiple visual and auditory markers, including speech prevalence (β=−0.68, P=.02, r2=0.40), overall expressivity (β=−0.46, P=.10, r2=0.27), and head movement measured as head pitch variability (β=−1.24, P=.006, r2=0.48) and head yaw variability (β=−0.54, P=.06, r2=0.32). Conclusions Digital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and linear associations with the severity of suicidal ideation.