Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis

• URL: http://arxiv.org/abs/2106.08445v1
• Date: Tue, 15 Jun 2021 21:33:59 GMT
• Title: Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis
• Authors: Maximilian Dietrich (1) and Silvia Seidlitz (2, 3), Nicholas Schreck (4), Manuel Wiesenfarth (4), Patrick Godau (2, 3), Minu Tizabi (2), Jan Sellner (2, 3), Sebastian Marx (1), Samuel Kn\"odler (5), Michael M. Allers (5), Leonardo Ayala (2, 7), Karsten Schmidt (8), Thorsten Brenner (8), Alexander Studier-Fischer (5), Felix Nickel (5), Beat P. M\"uller-Stich (5), Annette Kopp-Schneider (4), Markus A. Weigand (1) and Lena Maier-Hein (2, 6, 7) ((1) Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany, (2) Division of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany, (3) HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany (4) Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany, (5) Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany, (6) Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany, (7) Medical Faculty, Heidelberg University, Heidelberg, Germany, (8) Department of Anesthesiology and Intensive Care Medicine, University Hospital Essen, University Duisburg-Essen, Essen, Germany)
• Abstract summary: Automated machine learning-based diagnosis of sepsis based on hyperspectral imaging data has not been explored to date. While we were able to classify sepsis with an accuracy of over $98,%$ using the existing data, our research also revealed several subject-, therapy- and imaging-related confounders.
• Score: 28.77667667876798
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Sepsis is a leading cause of mortality and critical illness worldwide. While robust biomarkers for early diagnosis are still missing, recent work indicates that hyperspectral imaging (HSI) has the potential to overcome this bottleneck by monitoring microcirculatory alterations. Automated machine learning-based diagnosis of sepsis based on HSI data, however, has not been explored to date. Given this gap in the literature, we leveraged an existing data set to (1) investigate whether HSI-based automated diagnosis of sepsis is possible and (2) put forth a list of possible confounders relevant for HSI-based tissue classification. While we were able to classify sepsis with an accuracy of over $98\,\%$ using the existing data, our research also revealed several subject-, therapy- and imaging-related confounders that may lead to an overestimation of algorithm performance when not balanced across the patient groups. We conclude that further prospective studies, carefully designed with respect to these confounders, are necessary to confirm the preliminary results obtained in this study.

Related papers

• PerioDet: Large-Scale Panoramic Radiograph Benchmark for Clinical-Oriented Apical Periodontitis Detection [7.791916637642707]
  Apical periodontitis is a prevalent oral pathology that presents significant public health challenges.<n>Despite advances in automated diagnostic systems, the development ofCAD applications for apical periodontitis is still constrained by the lack of a large-scale, high-quality annotated dataset.<n>We release a large-scale panoramic radiograph benchmark called "PerioXrays", comprising 3,673 images and 5,662 meticulously annotated instances of apical periodontitis.
  arXiv  Detail & Related papers  (2025-07-25T04:53:09Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• Estimating the severity of dental and oral problems via sentiment classification over clinical reports [0.8287206589886879]
  Analyzing authors' sentiments in texts can be practical and useful in various fields, including medicine and dentistry. Deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network architecture, known as CNN-LSTM, was developed to detect severity level of patient's problem.
  arXiv  Detail & Related papers  (2024-01-17T14:33:13Z)
• Extending Machine Learning-Based Early Sepsis Detection to Different Demographics [1.2724528787590168]
  We compare two ensemble learning methods, LightGBM and XGBoost, using the public eICU-CRD dataset and a private South Korean St. Mary's Hospital's dataset. Our analysis reveals the effectiveness of these methods in addressing healthcare data imbalance and enhancing sepsis detection.
  arXiv  Detail & Related papers  (2023-11-07T20:02:52Z)
• PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage [2.602118060856794]
  Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage.
  arXiv  Detail & Related papers  (2023-08-21T07:18:51Z)
• ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data [1.370633147306388]
  Sepsis is a deadly condition affecting many patients in the hospital. We propose the use of Active Learning Recurrent Neural Networks (ALRts) for short temporal horizons to improve the prediction of irregularly sampled temporal events such as sepsis. We show that an active learning RNN model trained on limited data can form robust sepsis predictions comparable to models using the entire training dataset.
  arXiv  Detail & Related papers  (2022-12-13T04:31:49Z)
• Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques [111.165389441988]
  This work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI) Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.
  arXiv  Detail & Related papers  (2021-05-18T11:37:57Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks [4.339839287869652]
  Bovine Respiratory Disease Complex (BRDC) is a complex respiratory disease in cattle with multiple etiologies, including bacterial and viral. Current animal disease diagnostics is based on traditional tests such as bacterial culture, serolog, and Polymerase Chain Reaction (PCR) tests. We show that networks-based machine learning approaches can detect pathogen signature with up to 89.7% accuracy.
  arXiv  Detail & Related papers  (2020-07-24T22:30:18Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.