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Radiology Radiology

Deep Learning-Based Computer-Aided Detection System for Preoperative Chest Radiographs to Predict Postoperative Pneumonia.

In Academic radiology

RATIONALE AND OBJECTIVES : The role of preoperative chest radiography (CR) for prediction of postoperative pneumonia remains uncertain. We aimed to develop and validate a prediction model for postoperative pneumonia incorporating findings of preoperative CRs evaluated by a deep learning-based computer-aided detection (DL-CAD) system MATERIALS AND METHODS: This retrospective study included consecutive patients who underwent surgery between January 2019 and March 2020 and divided into development (surgery in 2019) and validation (surgery between January and March 2020) cohorts. Preoperative CRs obtained within 1-month before surgery were analyzed with a commercialized DL-CAD that provided probability values for the presence of 10 different abnormalities in CRs. Logistic regression models to predict postoperative pneumonia were built using clinical variables (clinical model), and both clinical variables and DL-CAD results for preoperative CRs (DL-CAD model). The discriminative performances of the models were evaluated by area under the receiver operating characteristic curves.

RESULTS : In development cohort (n = 19,349; mean age, 57 years; 11,392 men), DL-CAD results for pulmonary nodules (odds ratio [OR, for 1% increase in probability value], 1.007; p = 0.021), consolidation (OR, 1.019; p < 0.001), and cardiomegaly (OR, 1.013; p < 0.001) were independent predictors of postoperative pneumonia and were included in the DL-CAD model. In validation cohort (n = 4957; mean age, 56 years; 2848 men), the DL-CAD model exhibited a higher AUROC than the clinical model (0.843 vs. 0.815; p = 0.012).

CONCLUSION : Abnormalities in preoperative CRs evaluated by a DL-CAD were independent risk factors for postoperative pneumonia. Using DL-CAD results for preoperative CRs led to an improved prediction of postoperative pneumonia.

Lee Taehee, Hwang Eui Jin, Park Chang Min, Goo Jin Mo

2023-Mar-15

Deep learning, Pneumonia, Postoperative complications, Preoperative risk screening, Thoracic radiography

Pathology Pathology

The use of artificial intelligence to detect students' sentiments and emotions in gross anatomy reflections.

In Anatomical sciences education

Students' reflective writings in gross anatomy provide a rich source of complex emotions experienced by learners. However, qualitative approaches to evaluating student writings are resource heavy and timely. To overcome this, natural language processing, a nascent field of artificial intelligence that uses computational techniques for the analysis and synthesis of text, was used to compare health professional students' reflections on the importance of various regions of the body to their own lives and those of the anatomical donor dissected. A total of 1,365 anonymous writings (677 about a donor, 688 about self) were collected from 132 students. Binary and trinary sentiment analysis was performed, as well as emotion detection using the National Research Council Emotion Lexicon which classified text into eight emotions: anger, fear, sadness, disgust, surprise, anticipation, trust, and joy. The most commonly written about body regions were the hands, heart, and brain. The reflections had an overwhelming positive sentiment with major contributing words "love" and "loved". Predominant words such as "pain" contributed to the negative sentiments and reflected various ailments experienced by students and revealed through dissections of the donors. The top three emotions were trust, joy and anticipation. Each body region evoked a unique combination of emotions. Similarities between student self-reflections and reflections about their donor were evident suggesting a shared view of humanization and person-centeredness. Given the pervasiveness of reflections in anatomy, adopting a natural language processing approach to analysis could provide a rich source of new information related to students' previously undiscovered experiences and competencies.

Rechowicz Krzysztof J, Elzie Carrie A

2023-Mar-17

dissection, gross anatomy, health professions education, machine learning, natural language processing, reflections, reflective writing, sentiment analysis

Radiology Radiology

Modern Imaging of Aneurysmal Subarachnoid Hemorrhage.

In Radiologic clinics of North America

In this review, we discuss the imaging of aneurysmal subarachnoid hemorrhage (SAH). We discuss emergency brain imaging, aneurysm detection techniques, and the management of CTA-negative SAH. We also review the concepts of cerebral vasospasm and delayed cerebral ischemia that occurs after aneurysm rupture and their impact on patient outcomes. These pathologies are distinct, and the use of multimodal imaging modalities is essential for prompt diagnosis and management to minimize morbidity from these conditions. Lastly, new advances in artificial intelligence and advanced imaging modalities such as PET and MR imaging scans have been shown to improve the detection of aneurysms and potentially predict outcomes early in the course of SAH.

Levinson Simon, Pendharkar Arjun V, Gauden Andrew J, Heit Jeremy J

2023-May

AI, Aneurysm, Brain, CT, Hemorrhage, Imaging

Pathology Pathology

Noninferiority of Artificial Intelligence-Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics.

In Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

Image analysis assistance with artificial intelligence (AI) has become one of the great promises over recent years in pathology, with many scientific studies being published each year. Nonetheless, and perhaps surprisingly, only few image AI systems are already in routine clinical use. A major reason for this is the missing validation of the robustness of many AI systems: beyond a narrow context, the large variability in digital images due to differences in preanalytical laboratory procedures, staining procedures, and scanners can be challenging for the subsequent image analysis. Resulting faulty AI analysis may bias the pathologist and contribute to incorrect diagnoses and, therefore, may lead to inappropriate therapy or prognosis. In this study, a pretrained AI assistance tool for the quantification of Ki-67, estrogen receptor (ER), and progesterone receptor (PR) in breast cancer was evaluated within a realistic study set representative of clinical routine on a total of 204 slides (72 Ki-67, 66 ER, and 66 PR slides). This represents the cohort with the largest image variance for AI tool evaluation to date, including 3 staining systems, 5 whole-slide scanners, and 1 microscope camera. These routine cases were collected without manual preselection and analyzed by 10 participant pathologists from 8 sites. Agreement rates for individual pathologists were found to be 87.6% for Ki-67 and 89.4% for ER/PR, respectively, between scoring with and without the assistance of the AI tool regarding clinical categories. Individual AI analysis results were confirmed by the majority of pathologists in 95.8% of Ki-67 cases and 93.2% of ER/PR cases. The statistical analysis provides evidence for high interobserver variance between pathologists (Krippendorff's α, 0.69) in conventional immunohistochemical quantification. Pathologist agreement increased slightly when using AI support (Krippendorff α, 0.72). Agreement rates of pathologist scores with and without AI assistance provide evidence for the reliability of immunohistochemical scoring with the support of the investigated AI tool under a large number of environmental variables that influence the quality of the diagnosed tissue images.

Abele Niklas, Tiemann Katharina, Krech Till, Wellmann Axel, Schaaf Christian, Länger Florian, Peters Anja, Donner Andreas, Keil Felix, Daifalla Khalid, Mackens Marina, Mamilos Andreas, Minin Evgeny, Krümmelbein Michel, Krause Linda, Stark Maria, Zapf Antonia, Päpper Marc, Hartmann Arndt, Lang Tobias

2023-Mar

digital pathology, mammary carcinoma, surgical pathology

General General

Glutamatergic and GABAergic receptor modulation present unique electrophysiological fingerprints in a concentration-dependent and region-specific manner.

In eNeuro

Brain function depends on complex circuit interactions between excitatory and inhibitory neurons embedded in local and long-range networks. Systemic GABAA-receptor or NMDA-receptor modulation alters the excitatory/inhibitory balance (EIB), measurable with electroencephalography (EEG). However, EEG signatures are complex in localization and spectral composition. We developed and applied analytical tools to investigate the effects of two EIB modulators, MK801 (NMDAR-antagonist) and diazepam (GABAAR-modulator), on periodic and aperiodic EEG features in freely-moving male Sprague-Dawley rats. We investigated how, across three brain regions, EEG features are correlated with EIB modulation. We found that the periodic component was composed of seven frequency bands that presented region- and compound-dependent changes. The aperiodic component was also different between compounds and brain regions. Importantly, the parametrization into periodic and aperiodic components unveiled correlations between quantitative EEG and plasma concentrations of pharmacological compounds. MK-801 exposures were positively correlated with the slope of the aperiodic component. Concerning the periodic component, MK-801 exposures correlated negatively with the peak frequency of low-gamma oscillations but positively with those of high-gamma and high-frequency oscillations. As for the power, theta and low-gamma oscillations correlated negatively with MK-801, whereas mid-gamma correlated positively. Diazepam correlated negatively with the knee of the aperiodic component, positively to beta and negatively to low-gamma oscillatory power, and positively to the modal frequency of theta, low-, mid-, and high-gamma. In conclusion, correlations between exposures and pharmacodynamic effects can be better-understood thanks to the parametrization of EEG into periodic and aperiodic components. Such parametrization could be key in functional biomarker discovery.SIGNIFICANCE STATEMENTExcitatory-inhibitory balance (EIB) is compromised in neurological disorders. Our study demonstrates that pharmacologically-induced effects on EIB can be quantified by decomposing the qEEG PS signal into the oscillatory periodic and the 1/f aperiodic components. MK-801 and diazepam showed distinct signatures across brain regions and EEG components. Specific features of these components are sensitive to relatively small changes in measured exposure. This methodological approach and the features identified as sensitive to EIB modulation could be key for the development of new therapies and functional biomarkers in disorders with excitatory-inhibitory imbalance.

Gonzalez-Burgos Irene, Bainier Marie, Gross Simon, Schoenenberger Philipp, Ochoa-Martinez José A, Valencia Miguel, Redondo Roger L

2023-Mar-16

Diazepam, EEG, GABA, MK-801, NMDA, parametrization

General General

Review of Natural Language Processing in Pharmacology.

In Pharmacological reviews ; h5-index 63.0

Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora. The main objective of this work is to survey the recent use of NLP in the field of pharmacology. As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology. It has been used extensively, from intelligent searches through thousands of medical documents to finding traces of adversarial drug interactions in social media. We split our coverage into five categories to survey modern NLP methodology, commonly addressed tasks, relevant textual data, knowledge bases, and useful programming libraries. We split each of the five categories into appropriate subcategories, describe their main properties and ideas, and summarize them in a tabular form. The resulting survey presents a comprehensive overview of the area, useful to practitioners and interested observers. Significance Statement The main objective of this work is to survey the recent use of NLP in the field of pharmacology, in order to provide a comprehensive overview of the current state in the area after the rapid developments which occurred in the last few years. We believe the resulting survey to be useful to practitioners and interested observers in the domain.

Trajanov Dimitar, Trajkovski Vangel, Dimitrieva Makedonka, Dobreva Jovana, Jovanovik Milos, Klemen Matej, Žagar Aleš, Robnik-Šikonja Marko

2023-Mar-17

adverse drug reactions, drug analysis, drug discovery