Expert Interpretation Research Articles

Podnebne projekcije temperature zraka in padavin za porečja Ledave, Pesnice in Vipave do konca 21. stoletja

As part of the project ‚CeVoTak‘, Integrated Management of Small Water Retention and Soil Erosion Prevention Measures in Agricultural Catchments, we studied changing temperature and precipitation conditions up to the year 2100. The study was conducted on agricultural lands in the catchments of the Ledava and Pesnica rivers in the sub-Pannonian region and the Vipava river in the sub-Mediterranean region. A common climate database was used to create the climate projections - RCM (Regional Climate Model) simulations from the project EURO-CORDEX and scenarios RCP (Representative Concentration Pathway), RCP2.6, 4.5 and 8.5. The projections were prepared for three time periods 2011-2040; 2041-2070 and 2071-2100 for 6 different regional climate models for average, minimum and maximum air temperatures and precipitation. Analysis of the ensemble of model simulations for all scenarios shows similar results for the basin of all rivers, an increase in temperature (maximum in winter, minimum in spring), with high confidence for all scenarios and periods. Projections of precipitation are less reliable, but show an increase in annual precipitation due to the winter increase. The use of climate change projections with expert interpretation is essential for determining the vulnerability of individual areas and building resilience through the implementation of climate change adaptation.

Performance of tests based on the area under the ROC curve for multireader diagnostic data

One of the main objectives of disease prevention is to lower the healthcare costs and improve the quality of life. To achieve this, reliable diagnostic tools are needed. The diagnostic performance of a tool can be measured by the ROC curve and the AUC. However, some diagnostic tools such as MRI images are not objective, but depend on the interpretation of experts. Therefore, the accuracy of these tools may vary depending on who is interpreting them. To account for possible correlations when multiple readers collect data, Dorfman, Berbaum and Metz (1992) proposed using AUC pseudovalues from the jackknife sampling method and applying them to the mixed model to analyze the diagnostic reagent's accuracy. However, pseudovalues may go beyond the AUC range. Also, the random effect estimate may be negative due to a small number of readers. This paper develops tests based on AUC estimates and gives their asymptotic distribution. Moreover, a two-stage test is suggested to correct for negative random effect estimates. Four tests are created in total and their performance is evaluated by Monte Carlo simulations. The distributional assumption's robustness of these tests is checked, and their applicability is demonstrated by two real data sets.

Complex issues in vocational rehabilitation with functional capacity evaluation results

BACKGROUND: The American Physical Therapy Association (APTA) conducted a two-year process of literature review, writing, discussion, and debate within a panel of four subject matter experts in design, administration, and interpretation of Functional Capacity Evaluations (FCE). This process resulted in a Best Practice Guidelines document which was then adopted by the APTA. OBJECTIVE: This article provides additional clarification regarding best practices and standard methodology for functional capacity evaluations including standard terminology for use by physical therapists and vocational rehabilitation professionals in determining the types of work a patient has the capacity of performing. METHODS: The clarification of the role of the FCE, who is qualified to conduct an FCE, complexities such as ‘good effort’ and pain in the evaluation process, clarification of terms from different government agencies, and the implications for disability determination are discussed. RESULTS: Specific guidelines for best practice with FCE are suggested and demonstrated through case studies. CONCLUSION: The best practice guidelines are detailed here to set forth qualification standards for FCE examiners and to promote clinical excellence, accountability, and consistency in the design, administration, and interpretation of FCEs.

Artificial intelligence–based electrocardiogram analysis improves atrial arrhythmia detection from a smartwatch electrocardiogram

Abstract Aims Smartwatch electrocardiograms (SW ECGs) have been identified as a non-invasive solution to assess abnormal heart rhythm, especially atrial arrhythmias (AAs) that are related to stroke risk. However, the performance of these tools is limited and could be improved with the use of deep neural network (DNN) algorithms, particularly for specific populations encountered in clinical cardiology practice. Methods and results A total of 400 patients from the electrophysiology department of one tertiary care hospital were included in two similar clinical trials (respectively, 200 patients per study). Simultaneous ECGs were recorded with the watch and a 12-lead recording system during consultation or before and after an electrophysiology procedure if any. The SW ECGs were processed by using the DNN and with the Apple watch ECG software (Apple app). Corresponding 12-lead ECGs (12L ECGs) were adjudicated by an expert electrophysiologist. The performance of the DNN was assessed vs. the expert interpretation of the 12L ECG, and inconclusive rates were reported. Overall, the DNN and the Apple app presented, respectively, a sensitivity of 91% [95% confidence interval (CI) 85–95%] and 61% (95% CI 44–75%) with a specificity of 95% (95% CI 91–97%) and 97% (95% CI 93–99%) when compared with the physician 12L ECG interpretation. The DNN was able to provide a diagnosis on 99% of ECGs, while the Apple app was able to classify only 78% of strips (22% of inconclusive diagnosis). Conclusion In this study, by including patients from a cardiology department, a DNN-based algorithm applied to an SW ECG provided an accurate diagnosis for AA detection on virtually all tracings, outperforming the SW algorithm.

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer’s disease

Alzheimer’s disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

New method for taxonomic descriptions with coded notation, producing dynamic and interchangeable output.

A proposal for taxonomic species description notation is presented to replace the traditional descriptive texts for a coded matrix, avoiding redundant adjectives and subjective descriptions. This is an attempt to enhance the species description rate and to make the descriptions output available to other scientific disciplines, machine learning, interactive and computer-assisted identification keys, metadata analysis and its applications. The method consists of presenting the description of the overall morphology in a coded matrix, following a character list with detailed observed conditions for each character. The method is dynamic and open to amendments and new data addition as they become available. We test the new method describing five new species of Collembola Symphypleona of the genus Pararrhopalites as a generalized model and made the coded output available. We conclude that a coded taxonomic description is an advance to the traditional taxonomic text, with potential to enhance the global descriptions rate. The generated descriptions are dynamic, expandable and can be easily used in other fields of science, allowing non-experts to access the data for phylogenetic, biogeographic, ecological studies and metadata analysis. Even though an experienced taxonomist will always be necessary to make a detailed taxonomic description, it is a step forward to a general template to semi-automated taxon recognition and to future development of auxiliary tools for species description using machine learning and templates to speed up the time-consuming phase of schematic figures preparation, after the expert interpretations are done.

Real Estate Insights: Establishing transparency – setting AI standards in property valuation

PurposeThis real estate insight scrutinises the emerging role of Artificial Intelligence (AI), particularly Large Language Models (LLMs) like ChatGPT, in property valuation, advocating for establishing standardised reporting guidelines in AI-enabled property valuation.Design/methodology/approachThrough a conceptual exploration, this piece examines the shift towards AI integration in property valuation and the critical role of Explainable Artificial Intelligence (XAI) in this transition. It discusses the CANGARU framework for developing inclusive and universally applicable reporting guidelines and the importance of human oversight in validating AI-enabled valuations.FindingsIntegrating LLMs into property valuation signifies potential efficiency gains and task automation but also introduces risks related to accuracy, bias, and ethical dilemmas. Standardised reporting guidelines are identified as essential for responsibly harnessing AI’s benefits.Practical implicationsThe article underscores the need for the real estate industry to adopt transparent reporting practices, with valuers acting as expert interpreters of AI outputs. Emphasising error reporting in XAI not only aids in understanding AI-generated insights but also builds trust among stakeholders, ensuring AI’s ethical and effective application in property valuation.Originality/valueThis commentary contributes to the discourse on AI’s role in property valuation by focusing on the need for standard reporting guidelines that align with professional standards and legal frameworks. It advocates for a balanced approach to AI integration, where technological advancements complement traditional valuation expertise, ensuring accurate, fair, and transparent property valuations.

МЕТОДОЛОГИЧЕСКИЕ ПОДхОДы К ОЦЕНКЕ РАЗВИТИЯ ПРОФЕССИОНАЛьНых КОМПЕТЕНЦИЙ

The article deals with reviewing and classifying approaches to assessing the development of professional competencies. In the article considering algorithms for assessing the development of professional competencies. The author clarifying features, advantages and limitations of each group of methods. Most of the considered methods for assessing implementation assume the presence of significant budgetary and human resources for their implementation, which are available to large organizational entities. It is difficult to measure the development of professional competencies of employees of certain categories using methods suitable for small organizational entities (which may include intellectual labor specialists with specific human capital). Which raises the question of finding new comparable or adapting existing assessment methods for this purpose. One option is to develop and improve methods based on criterion-level and expert assessment principles, based on the tools of social sciences, for example, a sociological survey with the involvement of an expert for the qualitative interpretation of the collected data. Work in this direction provides opportunities for further development of research.

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.

MORPHOLOGY AND EVOLUTION OF ALVEOLAR WEATHERING CAVITIES ON SANDSTONE TORS OF THE UKRAINIAN BESKIDS

The paper systematizes morphological forms of alveolar weathering cavities and presents patterns of their evolution. We have studied the forms of tafoni (cavities 2–10 cm in size), which are found in the crags in the Ukrainian Beskydy. The factors that caused the formation of tafoni in our latitudes are humidity and the inherent specificity of selective weathering in sandstones due to structural and textural features formed during the formation of sediments. In winter periods, the role of frost weathering increases. The crags are composed of sandstones of the Yamna suite of the Palaeocene of the Lower Paleogene age. The lithological composition of the sandstones is represented by quartz and feldspar, which allows us to classify them as typical feldspar-quartz sandstones. The cement is clay-siliceous, sometimes with chlorite, of the basal or contact cementation type. In research region tafoni are distributed on rock surfaces in two ways: locally in small areas or covering large areas e.g. whole crag surface of particular aspect. In the first case, the forms are located in small groups of 10–30 pieces in separate parts of a tor wall. In the second case, they occupy the entire plane of the tor surface or half of the surface of particular exposure. Local groups of tafoni can be divided into three shape varieties: 1) vertical bands, 2) horizontal bands, and 3) lenticular bands. Tafoni concentrations that occupy the entire one plane of the tor can be located on it: a) chaotically, b) in a chain-like manner. The following shapes of particular cavities can be distinguished in the Ukrainian Beskydy: 1) rounded, 2) ellipsoid, 3) vertically elongated, and 4) subtriangular. In the Beskyds, the largest length of ellipsoidal cavities is 20–30 cm, the diameter of rounded cavities reaches 10 cm, and the greatest depth is 15 cm. Investigating the morphology of tafoni in the Beskydy, we distinguished the following stages of their evolution: emergence – deepening – expansion – merging – degradation – re-emergence. On the walls of the Beskydy tors, one can observe tafonic forms at various stages of evolution. We identified morpho-evolutionary varieties of tafoni: A) with all four clear edges, B) with three clear and one leveled wall, C) with all unclear walls. The confinement of cavities to slope exposures was also investigated. Cavities in the Carpathians develop more actively on walls of warm exposures than on cold ones. Tafoni forms on tors, their morphology, evolution and formation factors can be the subject of expert interpretations during geotourism excursions. Key words: cavities, tafoni; morphology, evolution, alveolar weathering; tors; Beskydy, Ukrainian Carpathians.

Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial

IntroductionSpirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of spirometry is variable in primary care....

Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline ATM sequence variants.

The ClinGen Hereditary Breast, Ovarian and Pancreatic Cancer (HBOP) Variant Curation Expert Panel (VCEP) is composed of internationally recognized experts in clinical genetics, molecular biology and variant interpretation. This VCEP made specifications for ACMG/AMP guidelines for the ataxia telangiectasia mutated (ATM) gene according to the Food and Drug Administration (FDA)-approved ClinGen protocol. These gene-specific rules for ATM were modified from the American College of Medical Genetics and Association for Molecular Pathology (ACMG/AMP) guidelines and were tested against 33 ATM variants of various types and classifications in a pilot curation phase. The pilot revealed a majority agreement between the HBOP VCEP classifications and the ClinVar-deposited classifications. Six pilot variants had conflicting interpretations in ClinVar and reevaluation with the VCEP's ATM-specific rules resulted in four that were classified as benign, one as likely pathogenic and one as a variant of uncertain significance (VUS) by the VCEP, improving the certainty of interpretations in the public domain. Overall, 28 the 33 pilot variants were not VUS leading to an 85% classification rate. The ClinGen-approved, modified rules demonstrated value for improved interpretation of variants in ATM.

Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level

Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF). We employed ResNet-based deep learning (DL) using ECG tracings and extreme gradient boosting (XGB) using ECG measurements. When evaluated on the first ECGs per episode of 97,631 holdout patients, the DL models had an area under the receiver operating characteristic curve (AUROC) of <80% for 3 CV conditions (PTE, SVT, UA), 80–90% for 8 CV conditions (CA, NSTEMI, VT, MVP, PHTN, AS, AF, HF) and an AUROC > 90% for 4 diagnoses (AVB, HCM, MS, STEMI). DL models outperformed XGB models with about 5% higher AUROC on average. Overall, ECG-based prediction models demonstrated good-to-excellent prediction performance in diagnosing common CV conditions.

Core characteristics of sublingual veins analysis and its relationship with hypertension.

The sublingual vein (SV) is a specialized diagnostic method used in Traditional Chinese Medicine (TCM). Despite its ability to objectively reflect blood flow, SV is often overlooked in clinical practice. This study aims to analyze the core characteristics of SV and investigate the in-depth relationship between its digital characteristics and hypertension. The goal is to find a link between SV and hypertension and break out of the current situation. Modern digital analysis techniques were applied to the traditional SV diagnostic theory. In a controlled study with 204 participants, the digital characteristics of SV were documented using TFDA-1, and its color value was analyzed using TDAS. Morphological characteristics of SV, such as trunklength, width, and tortuosity, were examined by combining computer vision with expert interpretation. This involved the application of automatic ranging methods and a rectangular approximation algorithm, which are novel approaches in the field of TCM. The t-test and Mann-Whitney U test were used to analyze the digital characteristics of SV in hypertension. Binary logistic regression and neural network models were established using machine learning to explore the deep relationship between SV characteristics and hypertension. There was a significant difference of the tortuosity of SV between the two groups (Z=-2.629, p= 0.009). The results revealed thick width of SV (OR = 2.64, 95% CI: 1.02-6.79) was the risk factor for hypertension. Addition of SV characteristics improved overall percent correct for hypertension prediction to 80%. TCM method of diagnosis of SV has been greatly expanded in terms of technical means, and the close relationship between SV and hypertension has been found in clinical data.

Comparative analysis of deep learning techniques for accurate stroke detection

Background: The traditional diagnosis of strokes through computed tomography (CT) heavily relies on radiologists’ expertise for accurate interpretation. However, the increasing demand for this critical task exceeds the available radiologist workforce, necessitating innovative solutions. This research addresses this challenge by introducing deep learning techniques to enhance the initial screening of stroke cases, thereby augmenting radiologists’ diagnostic capabilities. Objective: This study aims to compare four techniques for classifying stroke lesions in CT images. Materials and methods: Four distinct models-CNN-2-Model, LeNet, GoogleNet, and VGG-16-were trained using a dataset comprising 1,636 CT images, including 1,111 normal brain images and 525 stroke images. Seventy percent of the images were used to train the most effective deep learning model, and subsequently, these images were utilized to evaluate the performance of each model. The evaluation involved assessing accuracy, precision, sensitivity, specificity, F1 score, false positive rate, and AUC. Results: The evaluation process included a comprehensive statistical analysis of the models’ prediction results. The findings revealed that VGG-16 emerged as the top-performing deep learning model, achieving an impressive accuracy of 0.969, precision of 0.952, sensitivity of 0.952, specificity of 0.978, F1 score of 0.952, false positive rate of 0.022, and AUC of 0.965. Conclusion: In conclusion, deep learning techniques, particularly the VGG-16 model, demonstrate significant promise in enhancing the accuracy of stroke lesion classification in CT images. These findings underscore the potential of leveraging advanced technologies to address the growing challenges in stroke diagnosis and pave the way for more efficient and accessible healthcare solutions.

Development of a Physics Module for Measurement Material Assisted by Augmented Reality (AR) Using the Assemblr Edu Application

The background to this research is the lack of student interest in physics, with various underlying factors. Starting from the unavailability of learning modules in schools to the absence of measuring instruments, which results in students having difficulty calculating measurement results using measuring instruments that have never been seen or used before. Therefore, it is important to develop valid and practical modules, one of which is by using technology in teaching and learning activities, which is one of the steps in increasing the effectiveness of learning, with the help of Augmented Reality using the Assemblr Edu application and focusing on measurement material in class X. This research is research and development (R&amp;D) using the 4D model (Define, Design, Development, Dissemination). However, this research only reached development at the practicality stage. Based on the results of data analysis, validation from material, media and interpretation experts was found to be 94.5%, 83.3% and 91.6%, respectively, in the very valid category. And the practicality of the module by teacher and students was obtained at 98.6% and 86.4% in the very practical category.

Detection of myocardial infarction using Shannon energy envelope, FA-MVEMD and deterministic learning

Myocardial infarction (MI) poses a significant clinical challenge, necessitating expeditious and precise detection to mitigate potentially fatal outcomes. Current MI diagnosis predominantly relies on electrocardiography (ECG); however, it is fraught with limitations, including inter-observer variability and a reliance on expert interpretation. This study introduces an automated MI detection framework that capitalizes on hybrid signal processing methodologies and deterministic learning theory. The initial step involves the extraction of the Shannon energy envelope (SEE) and its derivative from a single-lead ECG. Integration of the SEE into the ECG’s phase portrait provides a means to capture the underlying nonlinear system dynamics. Subsequently, the application of fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) yields discriminative features originating from the most energetically dominant intrinsic mode components (IMFs) within the SEE. Profound dissimilarities are discernible between ECG signals recorded from healthy subjects and those afflicted with MI. In the subsequent phase, deterministic learning theory, implemented through neural networks, is employed to facilitate the classification of ECG signals into two distinct groups. The method’s efficacy is meticulously evaluated using the PTB diagnostic ECG database, resulting in a noteworthy average classification accuracy of 99.21%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} within a tenfold cross-validation framework. In summation, the findings affirm that the proposed features not only complement conventional ECG attributes but also align closely with the underlying dynamics of the ECG system, ultimately fortifying the automatic detection of MI. The imperative requirement for early and accurate MI diagnosis is addressed through our approach, offering a robust and dependable means to fulfill this pivotal clinical need.

Vision, body and interpretation in medical imaging diagnostics.

This article explores the profound impact of visualism and visual perception in the context of medical imaging diagnostics. It emphasizes the intricate interplay among vision, embodiment, subjectivity, language, and historicity within the realm of medical science and technology, with a specific focus on image consciousness. The study delves into the role of subjectivity in perception, facilitating the communication of opacity and historicity to the perceiving individual. Additionally, it scrutinizes the image interpretation process, drawing parallels to text interpretation and highlighting the influence of personal biases and individuality in medical practice. By revisiting Husserl's conceptualization of "image consciousness" and introducing the notion of "image theme", the paper seeks to establish a theoretical framework for making sense of images within the context of technological interpretation. A key objective is to enhance the phenomenology of technology through a systematic analysis of medical imaging diagnosis, contributing to an expanded epistemological foundation for medical practice. The article recognizes that the construction of medical knowledge incorporates subjective elements, especially within a historical context. The interpretation of images involves both instrumental and expert interpretation, with human subjectivity playing a crucial role. The article asserts that human creativity and conscious engagement are indispensable in interpreting all medical images.

Origen's Sources of Exegetical Authority: The Construction of an Inspired Exegete in the Pauline Lineage

AbstractIn this paper, I examine several sources of authority to which Origen laid claim as he set about the task of interpreting scripture. On occasion, in both his commentaries and his homilies, Origen provided accounts of his access to three different, though connected sources of authority that contributed to his self-presentation as an expert interpreter. These sources are as follows: 1) participation in the lineage of the apostles, particularly his exegetical role model, Paul. The second source of authority is a result of the first: 2) direct communication from the Logos, whom he understands to be Christ himself, and 3) angelic assistance and engagement with his pastoral exegetical project. In some instances, Origen even claimed to be on par with two of these sources of authority; he can be found claiming to have exegetical abilities similar to Paul's and, on some rare occasions, to have reached the same epistemological level as the angels. This analysis provides us not only with a case study of the kind of self-fashioning and authorising strategies used by elite scriptural exegetes in the first few centuries of the common era, but it also allows us to shift our focus away from exegetical methods towards the figure, personality and social position of the exegete him or herself. From this shift of focus we gain a better appreciation of a topic to which exegetical authors themselves dedicated considerable energy: just who it was that could occupy the office of authoritative Christian exegete and why.