This article investigates the origins of the experiences involved in the diagnostics (detection and normative evaluation) of biological entities in image-based medical praxis. Our specific research aim presupposes a vast discussion regarding the origins of knowledge in general, but is narrowed down to the alternatives of anthropomorphism and biomorphism. Accordingly, in the subsequent chapters we will make an attempt to investigate and illustrate what holds the diagnostic experiential situation together-biological regularities, manifestation via movement, conscious synthesis, causal categories, or something else. We argue that as long as knowledge originates out of practices, a promising way forward is to oscillate between the prominent although controversial ideas of the history of philosophy and observations of concrete human practices, such as, in our chosen example, image-based medical diagnostics of biological pathologies. Although a number of thinkers are involved in the discussion, Aristotle and Husserl are most important here as the representatives of historical paradigms on the matter. The body in this research was not taken solely as the physical entity (Körper) but rather as a transcendental, constitutive structure where diagnostic and biological processes synchronize in teleological movement (Leib). However, philosophical speculations are illustrated by actual radiograms, the interpretation of which brings us back to the aforementioned question of primacy regarding cognition.
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Mol Genet Metab Rep
March 2025
The Shmunis School of Biomedicine and Cancer Research, The George S. Wise Faculty for Life Sciences, Sagol School of Neurosciences, Tel Aviv University, 6997801 Tel Aviv, Israel.
Dihydrolipoamide dehydrogenase (DLD) deficiency is an autosomal recessive disorder characterized by a functional disruption in several critical mitochondrial enzyme complexes, including pyruvate dehydrogenase and α-ketoglutarate dehydrogenase. Despite DLD's pivotal role in cellular energy metabolism, detailed molecular and metabolic consequences of DLD deficiency (DLDD) remain poorly understood. This study represents the first in-depth multi-omics analysis, specifically metabolomic and transcriptomic, of fibroblasts derived from a DLD-deficient patient compound heterozygous for a common Ashkenazi Jewish variant (c.
View Article and Find Full Text PDFMed Image Anal
December 2024
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Department of Neuroscience and Biomedical Engineering, Aalto University, Finland; Department of Computer Science, Aalto University, Finland.
Recent years have seen a growing interest in methods for predicting an unknown variable of interest, such as a subject's diagnosis, from medical images depicting its anatomical-functional effects. Methods based on discriminative modeling excel at making accurate predictions, but are challenged in their ability to explain their decisions in anatomically meaningful terms. In this paper, we propose a simple technique for single-subject prediction that is inherently interpretable.
View Article and Find Full Text PDFPurpose: With the widespread introduction of dual energy computed tomography (DECT), applications utilizing the spectral information to perform material decomposition became available. Among these, a popular application is to decompose contrast-enhanced CT images into virtual non-contrast (VNC) or virtual non-iodine images and into iodine maps. In 2021, photon-counting CT (PCCT) was introduced, which is another spectral CT modality.
View Article and Find Full Text PDFJ Biomech
December 2024
Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, United States of America.
Medical image-based diagnostic techniques have become increasingly common in the clinic. Estimating fractional flow reserve in coronary stenoses from medical image data is among the most prominent examples. The modeling techniques used in these clinical tools require rigorous experimental validation yet there is currently no standardized, public toolset to help assess model credibility.
View Article and Find Full Text PDFTransl Vis Sci Technol
January 2025
Institute of the Electrical and Biomedical Engineering, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tyrol, Austria.
Purpose: To extract conjunctival bulbar redness from standardized high-resolution ocular surface photographs of a novel imaging system by implementing an image analysis pipeline.
Methods: Data from two trials (healthy; outgoing ophthalmic clinic) were collected, processed, and used to train a machine learning model for ocular surface segmentation. Various regions of interest were defined to globally and locally extract a redness biomarker based on color intensity.
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