Medical imaging articles within Nature Communications

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  • Comment
    | Open Access

    Growth chart studies of the human cerebellum, which is increasingly recognized as pivotal for cognitive development, are rare. Gaiser and colleagues utilized population-level neuroimaging to unveil cerebellar growth charts from childhood to adolescence, offering insights into brain development.

    • Zi-Xuan Zhou
    •  & Xi-Nian Zuo

  • Article
    | Open Access

    Data drift is the systematic change in the underlying distribution of input features in prediction models, and can cause deterioration in model performance. Here, the authors highlight the importance of detecting data drift in clinical settings and evaluate methods for detecting drift in medical image data.

    • Ali Kore
    • , Elyar Abbasi Bavil
    •  & Mohamed Abdalla

  • Article
    | Open Access

    Manual processes to produce ocular prostheses are time-consuming and yield varying quality. Here, authors present an automatic digital end-to-end process for custom ocular prostheses. It creates shape and appearance from image data of an OCT device and produces them using a full-colour 3D printer.

    • Johann Reinhard
    • , Philipp Urban
    •  & Mandeep S. Sagoo

  • Article
    | Open Access

    Accurate localization of abnormalities is crucial in the interpretation of chest X-rays. Here the authors present a deep learning framework for simultaneous localization of 14 thoracic abnormalities and calculation of cardiothoracic ratio, based on large X-ray dataset with bounding boxes created via a human-in-the-loop approach.

    • Weijie Fan
    • , Yi Yang
    •  & Dong Zhang

  • Article
    | Open Access

    Segmentation is an important fundamental task in medical image analysis. Here the authors show a deep learning model for efficient and accurate segmentation across a wide range of medical image modalities and anatomies.

    • Jun Ma
    • , Yuting He
    •  & Bo Wang

  • Article
    | Open Access

    Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.

    • Zhijian Yang
    • , Junhao Wen
    •  & Christos Davatzikos

  • Article
    | Open Access

    Real-time MRI provides accurate navigation and targeting for neurological interventions. Here, the authors propose a deep unrolled neural network for MRI reconstruction that enables real-time monitoring of remote-controlled brain interventions and can be integrated into diagnostic scanners.

    • Zhao He
    • , Ya-Nan Zhu
    •  & Yuan Feng

  • Article
    | Open Access

    Childhood obesity remains a global epidemic. Here, using objective measurements, the authors show that sedentary time increased from 6 h/day in childhood to 9 h/day in young adulthood, and was cumulatively associated with increased total and trunk fat mass. Both light or moderate-to-vigorous physical activity similarly partly reversed risk.

    • Andrew O. Agbaje
    • , Wei Perng
    •  & Tomi-Pekka Tuomainen

  • Article
    | Open Access

    Changes of left ventricular structure are used to predict morbidity and mortality in cardiovascular diseases. Here the authors conducted a study using advanced deep learning technology to analyze left ventricular regional wall thickness (LVRWT) in a large population, identifying 72 significant genetic loci linked to LVRWT traits.

    • Caibo Ning
    • , Linyun Fan
    •  & Xiaoping Miao

  • Article
    | Open Access

    Temporalis muscle thickness is a promising marker of lean muscle mass but has had limited utility due to its unknown normal growth trajectory and lack of standardized measurement. Here, the authors develop an automated deep learning pipeline to accurately measure temporalis muscle thickness from routine brain magnetic resonance imaging.

    • Anna Zapaishchykova
    • , Kevin X. Liu
    •  & Benjamin H. Kann

  • Article
    | Open Access

    Deep learning models can reflect and amplify human bias, potentially resulting inaccurate missed diagnoses. Here, the authors show that by leveraging the marginal pairwise equal opportunity, their model reduces bias in medical image classification by over 35% compared to baseline models, with minimal impact on AUC values.

    • Mingquan Lin
    • , Tianhao Li
    •  & Yifan Peng

  • Article
    | Open Access

    Assessment of different iron compounds in the living brain remains an open challenge. Here, the authors present a magnetic resonance imaging method which is sensitive to the iron homeostasis in the brain, and increases the detection of tumor tissue.

    • Shir Filo
    • , Rona Shaharabani
    •  & Aviv A. Mezer

  • Article
    | Open Access

    Here the authors present Distributed Synthetic Learning, a system that addresses data privacy, isolated data islands, and heterogeneity concerns in healthcare analytics by learning to generate state-of-the-art synthetic data for downstream tasks.

    • Qi Chang
    • , Zhennan Yan
    •  & Dimitris N. Metaxas

  • Article
    | Open Access

    Bone marrow adiposity is linked to disease, and it is unknown how it is modulated during space travel. Here, the authors show that astronauts returning from ISS missions had decreased marrow fat and increased hematopoiesis and bone formation, suggesting that adipose reserves in the bone marrow might be used as an energy source to counteract anemia and bone loss associated with space flight.

    • Tammy Liu
    • , Gerd Melkus
    •  & Guy Trudel

  • Article
    | Open Access

    Diagnosing shortcut learning in clinical models is difficult, as sensitive attributes may be causally linked with disease. Using multitask learning, the authors propose a method to directly test for the presence of shortcut learning in clinical ML systems.

    • Alexander Brown
    • , Nenad Tomasev
    •  & Jessica Schrouff

  • Review Article
    | Open Access

    Positron emission tomography is widely used to diagnose and monitor different disease states and interest in the technique has led to the demand for the development of new method for radiolabelling. Here the authors review the recent progress in the development of new PET probes.

    • Jian Rong
    • , Ahmed Haider
    •  & Steven H. Liang

  • Article
    | Open Access

    Risk assessment of lung disease mortality is currently limited. Here, authors show that deep learning can estimate lung disease mortality from a chest x-ray beyond risk factors, which may help to identify individuals at risk in screening and cancer populations.

    • Jakob Weiss
    • , Vineet K. Raghu
    •  & Hugo J.W.L. Aerts

  • Article
    | Open Access

    Accurate diagnosis of interstitial lung disease subtypes and prediction of patient survival rates remains challenging. Here, the authors develop AI algorithms to combine patient’s clinical history and longitudinal CT images to help clinicians diagnose and classify subtypes and dynamically predict disease progression and prognosis.

    • Xueyan Mei
    • , Zelong Liu
    •  & Yang Yang

  • Article
    | Open Access

    There is a lack of standardisation in slide microscopy imaging data. Here the authors report Slim, an open-source, web-based slide microscopy viewer implementing the Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a range of existing medical imaging systems.

    • Chris Gorman
    • , Davide Punzo
    •  & Markus D. Herrmann

  • Article
    | Open Access

    Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.

    • Sarthak Pati
    • , Ujjwal Baid
    •  & Spyridon Bakas

  • Comment
    | Open Access

    Very few of the COVID-19 ML models were fit for deployment in real-world settings. In this Comment, Huang et al. discuss the main steps required to develop clinically useful models in the context of an emerging infectious disease.

    • Shih-Cheng Huang
    • , Akshay S. Chaudhari
    •  & Matthew P. Lungren

  • Article
    | Open Access

    Triage is essential for the early diagnosis and reporting of emergency patients in the emergency department. Here, the authors develop an anomaly detection algorithm with a deep generative model that reprioritizes radiology worklists and provides lesion attention maps for brain CT images with critical findings.

    • Seungjun Lee
    • , Boryeong Jeong
    •  & Namkug Kim

  • Article
    | Open Access

    International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Here, the authors present the results of a biomedical image segmentation challenge, showing that a method capable of performing well on multiple tasks will generalize well to a previously unseen task.

    • Michela Antonelli
    • , Annika Reinke
    •  & M. Jorge Cardoso

  • Article
    | Open Access

    Although deep learning-based computer-aided diagnosis systems have recently achieved expert level performance, developing a robust model requires large, high-quality data with annotations. Here, the authors present a framework which can improve the performance of vision transformer simultaneously with self-supervision and self-training.

    • Sangjoon Park
    • , Gwanghyun Kim
    •  & Jong Chul Ye

  • Article
    | Open Access

    Ultrasound has had tremendous success in medical imaging. Here, Zhang et al demonstrate a batteryless implantable CMOS mote, to further augment the potential of ultrasound, providing additional information via the backscattering of the acoustic waves used in ultrasound imaging.

    • Yihan Zhang
    • , Prashant Muthuraman
    •  & Kenneth L. Shepard

  • Article
    | Open Access

    Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT images is an essential step in digital dentistry for precision dental healthcare. Here, the authors present a deep learning system for efficient, precise, and fully automatic segmentation of real-patient CBCT images presenting highly variable appearances.

    • Zhiming Cui
    • , Yu Fang
    •  & Dinggang Shen

  • Article
    | Open Access

    The toxicity of heavy metals for MRI contrast agents is an issue. Here, the authors report on the development of conjugated polymers nanoparticles based on paramagnetic polypyrrole to generate T2 MRI contrast effects by changing the interactions between polarons and water protons.

    • Qinrui Lin
    • , Yuhong Yang
    •  & Zhengzhong Shao

  • Article
    | Open Access

    High quality labels are important for model performance, evaluation and selection in medical imaging. As manual labelling is time-consuming and costly, the authors explore and benchmark various resource-effective methods for improving dataset quality.

    • Mélanie Bernhardt
    • , Daniel C. Castro
    •  & Ozan Oktay

  • Article
    | Open Access

    Studies in animal models have visualized drainage of interstitial or cerebrospinal fluid via lymphatic vessels, but there is limited data on in humans. Here, the authors non-invasively visualize lymphatic structures in the human brain, including evidence of lymphatic flow from cranial nerves to cervical lymph nodes, and differences by age and sex, without use of contrast agents.

    • Mehmet Sait Albayram
    • , Garrett Smith
    •  & Onder Albayram

  • Article
    | Open Access

    A low cost MRI scanner may have the potential to meet clinical needs at point of care or in low and middle income countries. Here the authors describe a low cost 0.055 Tesla MRI scanner that operates using a standard AC power outlet, and demonstrate its preliminary feasibility in diagnosing brain tumor and stroke.

    • Yilong Liu
    • , Alex T. L. Leong
    •  & Ed X. Wu

  • Article
    | Open Access

    Ultrasound is an important imaging modality for the detection and characterization of breast cancer, but it has been noted to have high false-positive rates. Here, the authors present an artificial intelligence system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound imaging.

    • Yiqiu Shen
    • , Farah E. Shamout
    •  & Krzysztof J. Geras

  • Article
    | Open Access

    Dual-energy X-ray absorptiometry and the Fracture Risk Assessment Tool are recommended tools for osteoporotic fracture risk evaluation, but are underutilized. Here, the authors present an opportunistic tool to identify fractures, predict bone mineral density and evaluate fracture risk using plain pelvis and lumbar spine radiographs.

    • Chen-I Hsieh
    • , Kang Zheng
    •  & Chang-Fu Kuo

  • Article
    | Open Access

    Performing multiple histological stains on a biopsy can be costly and time consuming. Here the authors present a method for the digital transformation of H&E stained tissue into special stains (e.g., PAS, Masson’s Trichrome and Jones silver stain), and demonstrate that it improves diagnoses over the use of H&E only.

    • Kevin de Haan
    • , Yijie Zhang
    •  & Aydogan Ozcan

  • Article
    | Open Access

    Unmasking the decision making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, the authors demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts.

    • Tianyu Han
    • , Sven Nebelung
    •  & Daniel Truhn

  • Article
    | Open Access

    Individuals vary considerably in how they are affected by stress. Here, the authors show that the severity of psychopathological symptoms triggered by prolonged real-life stress relate to fMRI-measured responsivity of the human brainstem arousal system and associated pupil responses.

    • Marcus Grueschow
    • , Nico Stenz
    •  & Birgit Kleim

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