In JMIR mHealth and uHealth

BACKGROUND : Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity alongside physiological alterations that wearables can capture.

OBJECTIVE : We explored whether physiological wearable data could predict: (aim 1) the severity of an acute affective episode at the intra-individual level, (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to the prior predictions, generalization across patients, and associations between affective symptoms and physiological data.

METHODS : We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded with a research-grade wearable (Empatica E4) across three consecutive timepoints (acute, response, and remission of episode). Euthymic patients and healthy controls (HC) were recorded during a single session (∼48 hours). Manic and depressive symptoms were assessed with standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), temperature (TEMP), blood volume pulse (BVP), heart rate (HR), and electrodermal activity (EDA). For data pre-processing, invalid physiological data were removed using a rule-based filter, channels were time-aligned at 1 second time units and then segmented window lengths of 32 seconds, since those parameters showed the best performances. We developed deep learning predictive models, assessed channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel fully automated method for analysis of physiological data from a research-grade wearable device, including a rule-based filter for invalid data and a viable supervised learning pipeline for time-series analyses.

RESULTS : 35 sessions (1,512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 HC (age 39.7±12.6; 31.6% female) were analyzed. (aim 1) The severity of mood episodes was predicted with moderate (62%-85%) accuracies. (aim 2) The polarity of episodes was predicted with moderate (70%) accuracy. The most relevant features for the former tasks were ACC, EDA, and HR. Kendall W showed fair agreement (0.383) in feature importance across classification tasks. Generalization of the former models were of overall low accuracy, with better results for the intra-individual models. "Increased motor activity" was associated with ACC (NMI>0.55), "aggressive behavior" with EDA (NMI=1.0), "insomnia" with ACC (NMI∼0.6), "motor inhibition" with ACC (NMI∼0.75), and "psychic anxiety" with EDA (NMI=0.52).

CONCLUSIONS : Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression respectively. These findings represent a promising pathway towards personalized psychiatry, in which physiological wearable data could allow early identification and intervention of mood episodes.

Anmella Gerard, Corponi Filippo, Li Bryan M, Mas Ariadna, Sanabra Miriam, Pacchiarotti Isabella, Valentí Marc, Grande Iria, Benabarre Antoni, Giménez-Palomo Anna, Garriga Marina, Agasi Isabel, Bastidas Anna, Cavero Myriam, Fernández-Plaza Tabatha, Arbelo Néstor, Bioque Miquel, García-Rizo Clemente, Verdolini Norma, Madero Santiago, Murru Andrea, Amoretti Silvia, Martínez-Aran Anabel, Ruiz Victoria, Fico Giovanna, De Prisco Michele, Oliva Vincenzo, Solanes Aleix, Radua Joaquim, Samalin Ludovic, Young Allan H, Vieta Eduard, Vergari Antonio, Hidalgo-Mazzei Diego

2023-Mar-07

In Medical physics ; h5-index 59.0

PURPOSE : Radiation damage on neurovascular bundles (NVBs) may be the cause of sexual dysfunction after radiotherapy for prostate cancer. However, it is challenging to delineate NVBs as organ-at-risks from planning CTs during radiotherapy. Recently, the integration of MR into radiotherapy made NVBs contour delineating possible. In this study, we aim to develop an MRI-based deep learning method for automatic NVB segmentation.

METHODS : The proposed method, named topological modulated network, consists of three subnetworks, i.e., a focal modulation, a hierarchical block and a topological fully convolutional network (FCN). The focal modulation is used to derive the location and bounds of left and right NVBs', namely the candidate volume-of-interests (VOIs). The hierarchical block aims to highlight the NVB boundaries information on derived feature map. The topological FCN then segments the NVBs inside the VOIs by considering the topological consistency nature of the vascular delineating. Based on the location information of candidate VOIs, the segmentations of NVBs can then be brought back to the input MRI's coordinate system.

RESULTS : A five-fold cross-validation study was performed on 60 patient cases to evaluate the performance of the proposed method. The segmented results were compared with manual contours. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD₉₅ ) are (left NVB) 0.81 ± 0.10, 1.49 ± 0.88 mm, and (right NVB) 0.80 ± 0.15, 1.54 ± 1.22 mm, respectively.

CONCLUSION : We proposed a novel deep learning-based segmentation method for NVBs on pelvic MR images. The good segmentation agreement of our method with the manually drawn ground truth contours supports the feasibility of the proposed method, which can be potentially used to spare NVBs during proton and photon radiotherapy and thereby improve the quality of life for prostate cancer patients. This article is protected by copyright. All rights reserved.

Lei Yang, Wang Tonghe, Roper Justin, Tian Sibo, Patel Pretesh, Bradley Jeffrey D, Jani Ashesh B, Liu Tian, Yang Xiaofeng

2023-Mar-20

MRI, Neurovascular bundles, deep learning, segmentation

In Nature machine intelligence

Wang Ge, Badal Andreu, Jia Xun, Maltz Jonathan S, Mueller Klaus, Myers Kyle J, Niu Chuang, Vannier Michael, Yan Pingkun, Yu Zhou, Zeng Rongping

2022-Nov

In Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine

Karacaoglu Fatma, Kolsuz Mehmet Eray, Bagis Nilsun, Evli Cengiz, Orhan Kaan

2023-Mar-20

Artificial intelligence, alveolar bone defect, deep learning, periapical radiography, periodontal defect

In Genetics in medicine : official journal of the American College of Medical Genetics

PURPOSE : The analysis of exome and genome sequencing data for the diagnosis of rare diseases is challenging and time-consuming. In this study, we evaluated a machine learning model for automating variant prioritization for diagnosing rare genetic diseases in the Baylor Genetics clinical laboratory.

METHODS : The automated analysis model was developed using a supervised learning approach based on thousands of manually curated variants. The model was evaluated on two cohorts. The model accuracy was determined using a retrospective cohort comprised of 180 randomly selected exome cases (57 singletons, 123 trios), all of which were previously diagnosed and solved by manual interpretation. Diagnostic yield with the modified workflow was estimated using a prospective "production" cohort of 334 consecutive clinical cases.

RESULTS : The model accurately pinpointed all manually reported variants as candidates. The reported variants were ranked in top-ten candidate variants in 98.4% (121/123) of trio cases, in 93.0% (53/57) of single proband cases, and 96.7% (174/180) of all cases. The accuracy of the model was reduced in some cases due to incomplete variant calling (e.g., copy number variants) or incomplete phenotypic description.

CONCLUSION : The automated model for case analysis assists clinical genetic laboratories in prioritizing candidate variants effectively. The use of such technology may facilitate the interpretation of genomic data for a large number of patients in the era of precision medicine.

Meng Linyan, Attali Ruben, Talmy Tomer, Regev Yakir, Mizrahi Niv, Smirin-Yosef Pola, Vossaert Liesbeth, Taborda Christian, Santana Michael, Machol Ido, Xiao Rui, Dai Hongzheng, Eng Christine, Xia Fan, Tzur Shay

2023-Mar-16

clinical genomics

In Expert review of vaccines ; h5-index 40.0

BACKGROUND : Monitoring the risk of intussusception after the introduction of rotavirus vaccines is recommended by the World Health Organization (WHO). Although the validity of intussusception monitoring using electronic health records (EHRs) has been confirmed previously, no similar studies have been conducted in China. We aimed to verify the diagnosis and determine an algorithm with the best performance for identification of intussusception using Chinese EHR databases.

RESEARCH DESIGN AND METHODS : Using the Regional Health Information Platform in Ningbo, patients aged 0-72 months from 2015 to 2021 with any related visits for intussusception were included. The algorithms were based on diagnostic codes or keywords in different clinical scenarios, and their performance was evaluated with positive predictive value (PPV) and sensitivity in line with the Brighton guidelines.

RESULTS : Brighton level 1 intussusception was confirmed in 2958 patients with 3246 episodes. Fine-tuned algorithms combining the appearance of the relevant ICD-10 codes or the Chinese keyword 'Chang Tao' in any diagnostic reports with the results of enema treatments or related surgeries showed the highest sensitivity, while the highest PPV was obtained by further criteria based on typical radiographic appearances.

CONCLUSION : Intussusception could be identified and validated internally using EHRs in Ningbo.

Deng Siwei, Liu Zhike, Yang Junting, Zhang Liang, Shou Tiejun, Zhu Jianming, He Yan, Ma Rui, Li Ning, Xu Guozhang, Zhan Siyan

2023

Algorithms, China, electronic health records, intussusception, post-market monitoring, verification study