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Surgery Surgery

How to Cope with Big Data in Functional Analysis of the Esophagus.

In Visceral medicine

Introduction : Esophageal motility disorders have a severe impact on patients' quality of life. While high-resolution manometry (HRM) is the gold standard in the diagnosis of esophageal motility disorders, intermittently occurring muscular deficiencies often remain undiscovered if they do not lead to an intense level of discomfort or cause suffering in patients. Ambulatory long-term HRM allows us to study the circadian (dys)function of the esophagus in a unique way. With the prolonged examination period of 24 h, however, there is an immense increase in data which requires personnel and time for evaluation not available in clinical routine. Artificial intelligence (AI) might contribute here by performing an autonomous analysis.

Methods : On the basis of 40 previously performed and manually tagged long-term HRM in patients with suspected temporary esophageal motility disorders, we implemented a supervised machine learning algorithm for automated swallow detection and classification.

Results : For a set of 24 h of long-term HRM by means of this algorithm, the evaluation time could be reduced from 3 days to a core evaluation time of 11 min for automated swallow detection and clustering plus an additional 10-20 min of evaluation time, depending on the complexity and diversity of motility disorders in the examined patient. In 12.5% of patients with suggested esophageal motility disorders, AI-enabled long-term HRM was able to reveal new and relevant findings for subsequent therapy.

Conclusion : This new approach paves the way to the clinical use of long-term HRM in patients with temporary esophageal motility disorders and might serve as an ideal and clinically relevant application of AI.

Jell Alissa, Kuttler Christina, Ostler Daniel, Hüser Norbert


Artificial intelligence, Automated swallow detection, Big data, Classification, Esophagus, Long-term high-resolution manometry

General General

Shaping the Future of Digitally Enabled Health and Care.

In Pharmacy (Basel, Switzerland)

People generally need more support as they grow older to maintain healthy and active lifestyles. Older people living with chronic conditions are particularly dependent on healthcare services. Yet, in an increasingly digital society, there is a danger that efforts to drive innovations in eHealth will neglect the needs of those who depend on healthcare the most-our ageing population. The SHAPES (Smart and Healthy Ageing through People Engaging in Supportive Systems) Innovation Action aims to create an open European digital platform that facilitates the provision of meaningful, holistic support to older people living independently. A pan-European pilot campaign will evaluate a catalogue of digital solutions hosted on the platform that have been specifically adapted for older people. 'Medicines control and optimisation' is one of seven themes being explored in the campaign and will investigate the impact of digital solutions that aim to optimise medicines use by way of fostering effective self-management, while facilitating timely intervention by clinicians based on remote monitoring and individualised risk assessments powered by artificial intelligence. If successful, the SHAPES Innovation Action will lead to a greater sense of self-sufficiency and empowerment in people living with chronic conditions as they grow older.

Spargo Maureen, Goodfellow Nicola, Scullin Claire, Grigoleit Sonja, Andreou Andreas, Mavromoustakis Constandinos X, Guerra Bárbara, Manso Marco, Larburu Nekane, Villacañas Óscar, Fleming Glenda, Scott Michael


digital solutions, medicines management, patient safety, remote monitoring

General General

Distant Domain Transfer Learning for Medical Imaging.

In IEEE journal of biomedical and health informatics

Medical image processing is one of the most important topics in the field of the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. However, conventional deep learning has two major drawbacks: 1) insufficient training data and 2) the domain mismatch between the training data and the testing data. In this paper, we propose a novel transfer learning framework for medical image classification. Moreover, we apply our method to a recent issue (Coronavirus Diagnose). Several studies indicate that lung Computed Tomography (CT) images can be used for a fast and accurate COVID-19 diagnosis. However, well-labeled training data sets cannot be easily accessed due to the novelty of the disease and the privacy policies. The proposed method has two components: Reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. This study is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). In this study, we develop a DDTL model for COVID-19 diagnose using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data. The main contributions of this study are: 1) the proposed method benefits from unlabeled data in distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than "non-transfer" algorithms, and 8% higher than existing transfer and distant transfer algorithms.

Niu Shuteng, Liu Meryl, Liu Yongxin, Wang Jian, Song Houbing


Radiology Radiology

Artificial Intelligence-Based Polyp Detection in Colonoscopy: Where Have We Been, Where Do We Stand, and Where Are We Headed?

In Visceral medicine

Background : In the past, image-based computer-assisted diagnosis and detection systems have been driven mainly from the field of radiology, and more specifically mammography. Nevertheless, with the availability of large image data collections (known as the "Big Data" phenomenon) in correlation with developments from the domain of artificial intelligence (AI) and particularly so-called deep convolutional neural networks, computer-assisted detection of adenomas and polyps in real-time during screening colonoscopy has become feasible.

Summary : With respect to these developments, the scope of this contribution is to provide a brief overview about the evolution of AI-based detection of adenomas and polyps during colonoscopy of the past 35 years, starting with the age of "handcrafted geometrical features" together with simple classification schemes, over the development and use of "texture-based features" and machine learning approaches, and ending with current developments in the field of deep learning using convolutional neural networks. In parallel, the need and necessity of large-scale clinical data will be discussed in order to develop such methods, up to commercially available AI products for automated detection of polyps (adenoma and benign neoplastic lesions). Finally, a short view into the future is made regarding further possibilities of AI methods within colonoscopy.

Key Messages : Research of image-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.

Wittenberg Thomas, Raithel Martin


AI, Adenoma and polyp detection, Artificial intelligence, Colonoscopy, History

Radiology Radiology

New advances in CT imaging of pancreas diseases: a narrative review.

In Gland surgery

Computed tomography (CT) plays a pivotal role as a diagnostic tool in many diagnostic and diffuse pancreatic diseases. One of the major limits of CT is related to the radiation exposure of young patients undergoing repeated examinations. Besides the standard CT protocol, the most recent technological advances, such as low-voltage acquisitions with high performance X-ray tubes and iterative reconstructions, allow for significant optimization of the protocol with dose reduction. The variety of CT tools are further expanded by the introduction of dual energy: the production of energy-selective images (i.e., virtual monochromatic images) improves the image contrast and lesion detection while the material-selective images (e.g., iodine maps or virtual unenhanced images) are valuable for lesion detection and dose reduction. The perfusion techniques provide diagnostic and prognostic information lesion and parenchymal vascularization and interstitium. Both dual energy and perfusion CT have the potential for pushing the limits of conventional CT from morphological evaluation to quantitative imaging applied to inflammatory and oncological diseases. Advances in post-processing of CT images, such as pancreatic volumetry, texture analysis and radiomics provide relevant information for pancreatic function but also for the diagnosis, management and prognosis of pancreatic neoplasms. Artificial intelligence is promising for optimization of the workflow in qualitative and quantitative analyses. Finally, basic concepts on the role of imaging on screening of pancreatic diseases will be provided.

Agostini Andrea, Borgheresi Alessandra, Bruno Federico, Natella Raffaele, Floridi Chiara, Carotti Marina, Giovagnoni Andrea


CT quantitative, Pancreas, dual energy CT, perfusion CT, texture analysis

General General

Updates in using a molecular classifier to identify usual interstitial pneumonia in conventional transbronchial lung biopsy samples.

In Breathe (Sheffield, England)

A molecular classifier using a machine-learning algorithm based on genomic data could provide an objective method to aid clinicians and multidisciplinary teams to establish the diagnosis of IPF in less-invasive transbronchial lung biopsy samples

Crespo Andrea, Alfaro Tiago, Somogyi Vivien, Kreuter Michael