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General General

Decreased Resting-State Alpha Self-Synchronization in Depressive Disorder.

In Clinical EEG and neuroscience

Background. Depression disorder has been associated with altered oscillatory brain activity. The common methods to quantify oscillatory activity are Fourier and wavelet transforms. Both methods have difficulties distinguishing synchronized oscillatory activity from nonrhythmic and large-amplitude artifacts. Here we proposed a method called self-synchronization index (SSI) to quantify synchronized oscillatory activities in neural data. The method considers temporal characteristics of neural oscillations, amplitude, and cycles, to estimate the synchronization value for a specific frequency band. Method. The recorded electroencephalography (EEG) data of 45 depressed and 55 healthy individuals were used. The SSI method was applied to each EEG electrode filtered in the alpha frequency band (8-13 Hz). The multiple linear regression model was used to predict depression severity (Beck Depression Inventory-II scores) using alpha SSI values. Results. Patients with severe depression showed a lower alpha SSI than those with moderate depression and healthy controls in all brain regions. Moreover, the alpha SSI values negatively correlated with depression severity in all brain regions. The regression model showed a significant performance of depression severity prediction using alpha SSI. Conclusion. The findings support the SSI measure as a powerful tool for quantifying synchronous oscillatory activity. The data examined in this article support the idea that there is a strong link between the synchronization of alpha oscillatory neural activities and the level of depression. These findings yielded an objective and quantitative depression severity prediction.

Mohammadi Yousef, Kafraj Mohadeseh Shafiei, Graversen Carina, Moradi Mohammad Hassan

2023-Mar-21

Beck Depression Inventory-II, EEG, alpha self-synchronization, depression severity, neural oscillations

Radiology Radiology

Attention fusion network with self-supervised learning for staging of osteonecrosis of the femoral head (ONFH) using multiple MR protocols.

In Medical physics ; h5-index 59.0

BACKGROUND : Osteonecrosis of the femoral head (ONFH) is characterized as bone cell death in the hip joint, involving a severe pain in the groin. The staging of ONFH is commonly based on MRI and CT, which are important for establishing effective treatment plans. There have been some attempts to automate ONFH staging using deep learning, but few of them used only MR images.

PURPOSE : To propose a deep learning model for MR-only ONFH staging, which can reduce additional cost and radiation exposure from the acquisition of CT images.

METHODS : We integrated information from the MR images of five different imaging protocols by a newly proposed attention fusion method, which was composed of intra-modality attention and inter-modality attention. In addition, a self-supervised learning was used to learn deep representations from a large amount of paired MR-CT dataset. The encoder part of the MR-CT translation network was used as a pretraining network for the staging, which aimed to overcome the lack of annotated data for staging. Ablation studies were performed to investigate the contributions of each proposed method. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the networks.

RESULTS : Our model improved the performance of the four-way classification of the association research circulation osseous (ARCO) stage using MR images of the multiple protocols by 6.8%p in AUROC over a plain VGG network. Each proposed method increased the performance by 4.7%p (self-supervised learning) and 2.6%p (attention fusion) in AUROC, which was demonstrated by the ablation experiments.

CONCLUSIONS : We have shown the feasibility of the MR-only ONFH staging by using self-supervised learning and attention fusion. A large amount of paired MR-CT data in hospitals can be used to further improve the performance of the staging, and the proposed method has potential to be used in the diagnosis of various diseases that require staging from multiple MR protocols. This article is protected by copyright. All rights reserved.

Kim Bomin, Lee Geun Young, Park Sung-Hong

2023-Mar-21

attention fusion, mr-only staging, multiple mr protocols, osteonecrosis of femoral head, self-supervised learning

General General

Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT.

In ArXiv

Artificial intelligence-based methods have generated substantial interest in nuclear medicine. An area of significant interest has been using deep-learning (DL)-based approaches for denoising images acquired with lower doses, shorter acquisition times, or both. Objective evaluation of these approaches is essential for clinical application. DL-based approaches for denoising nuclear-medicine images have typically been evaluated using fidelity-based figures of merit (FoMs) such as RMSE and SSIM. However, these images are acquired for clinical tasks and thus should be evaluated based on their performance in these tasks. Our objectives were to (1) investigate whether evaluation with these FoMs is consistent with objective clinical-task-based evaluation; (2) provide a theoretical analysis for determining the impact of denoising on signal-detection tasks; (3) demonstrate the utility of virtual clinical trials (VCTs) to evaluate DL-based methods. A VCT to evaluate a DL-based method for denoising myocardial perfusion SPECT (MPS) images was conducted. The impact of DL-based denoising was evaluated using fidelity-based FoMs and AUC, which quantified performance on detecting perfusion defects in MPS images as obtained using a model observer with anthropomorphic channels. Based on fidelity-based FoMs, denoising using the considered DL-based method led to significantly superior performance. However, based on ROC analysis, denoising did not improve, and in fact, often degraded detection-task performance. The results motivate the need for objective task-based evaluation of DL-based denoising approaches. Further, this study shows how VCTs provide a mechanism to conduct such evaluations using VCTs. Finally, our theoretical treatment reveals insights into the reasons for the limited performance of the denoising approach.

Yu Zitong, Rahman Md Ashequr, Laforest Richard, Schindler Thomas H, Gropler Robert J, Wahl Richard L, Siegel Barry A, Jha Abhinav K

2023-Mar-16

General General

Understanding metric-related pitfalls in image analysis validation.

In ArXiv

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

Reinke Annika, Tizabi Minu D, Baumgartner Michael, Eisenmann Matthias, Heckmann-Nötzel Doreen, Kavur A Emre, Rädsch Tim, Sudre Carole H, Acion Laura, Antonelli Michela, Arbel Tal, Bakas Spyridon, Benis Arriel, Blaschko Matthew, Büttner Florian, Cardoso M Jorge, Cheplygina Veronika, Chen Jianxu, Christodoulou Evangelia, Cimini Beth A, Collins Gary S, Farahani Keyvan, Ferrer Luciana, Galdran Adrian, van Ginneken Bram, Glocker Ben, Godau Patrick, Haase Robert, Hashimoto Daniel A, Hoffman Michael M, Huisman Merel, Isensee Fabian, Jannin Pierre, Kahn Charles E, Kainmueller Dagmar, Kainz Bernhard, Karargyris Alexandros, Karthikesalingam Alan, Kenngott Hannes, Kleesiek Jens, Kofler Florian, Kooi Thijs, Kopp-Schneider Annette, Kozubek Michal, Kreshuk Anna, Kurc Tahsin, Landman Bennett A, Litjens Geert, Madani Amin, Maier-Hein Klaus, Martel Anne L, Mattson Peter, Meijering Erik, Menze Bjoern, Moons Karel G M, Müller Henning, Nichyporuk Brennan, Nickel Felix, Petersen Jens, Rafelski Susanne M, Rajpoot Nasir, Reyes Mauricio, Riegler Michael A, Rieke Nicola, Saez-Rodriguez Julio, Sánchez Clara I, Shetty Shravya, van Smeden Maarten, Summers Ronald M, Taha Abdel A, Tiulpin Aleksei, Tsaftaris Sotirios A, Calster Ben Van, Varoquaux Gaël, Wiesenfarth Manuel, Yaniv Ziv R, Jäger Paul F, Maier-Hein Lena

2023-Feb-09

General General

Roadmap on Deep Learning for Microscopy.

In ArXiv

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

Volpe Giovanni, Wählby Carolina, Tian Lei, Hecht Michael, Yakimovich Artur, Monakhova Kristina, Waller Laura, Sbalzarini Ivo F, Metzler Christopher A, Xie Mingyang, Zhang Kevin, Lenton Isaac C D, Rubinsztein-Dunlop Halina, Brunner Daniel, Bai Bijie, Ozcan Aydogan, Midtvedt Daniel, Wang Hao, Sladoje Nataša, Lindblad Joakim, Smith Jason T, Ochoa Marien, Barroso Margarida, Intes Xavier, Qiu Tong, Yu Li-Yu, You Sixian, Liu Yongtao, Ziatdinov Maxim A, Kalinin Sergei V, Sheridan Arlo, Manor Uri, Nehme Elias, Goldenberg Ofri, Shechtman Yoav, Moberg Henrik K, Langhammer Christoph, Špačková Barbora, Helgadottir Saga, Midtvedt Benjamin, Argun Aykut, Thalheim Tobias, Cichos Frank, Bo Stefano, Hubatsch Lars, Pineda Jesus, Manzo Carlo, Bachimanchi Harshith, Selander Erik, Homs-Corbera Antoni, Fränzl Martin, de Haan Kevin, Rivenson Yair, Korczak Zofia, Adiels Caroline Beck, Mijalkov Mite, Veréb Dániel, Chang Yu-Wei, Pereira Joana B, Matuszewski Damian, Kylberg Gustaf, Sintorn Ida-Maria, Caicedo Juan C, Cimini Beth A, Bell Muyinatu A Lediju, Saraiva Bruno M, Jacquemet Guillaume, Henriques Ricardo, Ouyang Wei, Le Trang, Gómez-de-Mariscal Estibaliz, Sage Daniel, Muñoz-Barrutia Arrate, Lindqvist Ebba Josefson, Bergman Johanna

2023-Mar-07

General General

Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER Initiative.

In Research square

Background Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors. Method In this retrospective cohort study, we investigated two large-scale PCORnet clinical research networks, INSIGHT and OneFlorida+, including 11 million patients in the New York City area and 16.8 million patients from Florida, to develop machine learning prediction models for those who are at risk for newly incident PASC and to identify factors associated with newly incident PASC conditions. Adult patients aged 20 with SARS-CoV-2 infection and without recorded infection between March 1 st , 2020, and November 30 th , 2021, were used for identifying associated factors with incident PASC after removing background associations. The predictive models were developed on infected adults. Results We find several incident PASC, e.g., malnutrition, COPD, dementia, and acute kidney failure, were associated with severe acute SARS-CoV-2 infection, defined by hospitalization and ICU stay. Older age and extremes of weight were also associated with these incident conditions. These conditions were better predicted (C-index >0.8). Moderately predictable conditions included diabetes and thromboembolic disease (C-index 0.7-0.8). These were associated with a wider variety of baseline conditions. Less predictable conditions included fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions This observational study suggests that a set of likely risk factors for different PASC conditions were identifiable from EHRs, predictability of different PASC conditions was heterogeneous, and using machine learning-based predictive models might help in identifying patients who were at risk of developing incident PASC.

Zang Chengxi, Hou Yu, Schenck Edward, Xu Zhenxing, Zhang Yongkang, Xu Jie, Bian Jiang, Morozyuk Dmitry, Khullar Dhruv, Nordvig Anna, Shenkman Elizabeth, Rothman Russel, Block Jason, Lyman Kristin, Zhang Yiye, Varma Jay, Weiner Mark, Carton Thomas, Wang Fei, Kaushal Rainu, Consortium The Recover

2023-Mar-08