Search

Summary: Background & Aims: In recent years, artificial intelligence (AI) models based on chest radiography have gained attention in various fields, including cardiac function, age estimation, and clinical assessment during hospitalization. The Global Leadership Initiative on Malnutrition (GLIM) criteria are widely used in malnutrition diagnosis. The objective of this study is to examine the predictive ability of chest radiographs using deep learning techniques in relation to hematological parameters that are already known to be associated with malnutrition, as well as malnutrition scores, including GLIM criteria. Methods: A total of 3701 older patients (age ≥65 years) were admitted to our hospital from January 2021 to January 2022, and after excluding those with missing data, participants (N=2862) were enrolled. Chest Radiographs, Basic information (height, weight, age, and sex), hematological parameters (albumin, hemoglobin, lymphocyte count, and C-reactive protein), malnutritional assessments: GLIM severity, Geriatric Nutritional Risk Index (GNRI), modified Controlling Nutritional status (mCONUT), and Subjective Global Assessment (SGA), and Nutritional Support Team (NST) intervention were extracted and utilized as training and validation data. We used chest radiographic image's matrix as explanatory variables for numerical (hematological parameters) or categorical (scoring) nutritional data as objective variables. A previously reported deep learning model helped construct a chest radiography-based prediction model from the training data. The predicted data were evaluated by computing the correlation coefficients and area under the curve (AUC). Results: As a numerical variables analysis, albumin and hemoglobin predictions were relatively accurate (R=0.71, 0.74). As a categorical malnutritional prediction, chest radiograph-based AI effectively aided nutritional decisions based on GNRI (AUC: 0.88, 0.83, 0.88, and 0.90), SGA (0.75, 0.71, and 0.88), mCONUT (0.84, 0.88, 0.90, and 0.88), and GLIM severity (0.88, 0.82, and 0.85) class indices. The Class activation maps (CAM) analysis identified variation in the X-ray sites for each malnutritional AI prediction, with some sites in agreement and others in disagreement. Conclusion: Deep learning-based chest radiographic AI has the potential to more accurately reflect malnutrition scoring than hematologic parameters. Furthermore, it can predict outcomes and assess malnutrition, including GLIM criteria. It is anticipated that AI will be integrated into the NST workflow in the future.

Summary: Background & Aims: Malnutrition in the elderly, frequently and significantly affects both physical functioning and cognition, as well as incurs direct and indirect costs to society. Guidelines recommend rapid nutritional intervention and initiation of nutritional therapy within 24–48 hours of admission. Height and weight information is essential for proper nutritional assessment; however, it is difficult to obtain individual the height and weight of bedridden elderly patients directly. This study aimed to illustrate the potential of a convolutional neural network model to assess the height and weight based on chest radiographs. Methods: We retrospectively evaluated radiographs obtained over 15 years of follow-up. Overall, 6,453 radiographs from male patients, and 7,879 from female patients were included in the analysis. A convolutional neural network was used to predict the height and weight of the patients (Juzen NST). A ResNet152 classifier was trained using Fastai (V1.0) running on PyTorch to predict the height and weight. Training was performed for four epochs using validation without augmentation. Results: The correlation coefficients between the predicted and measured values using the height prediction model for males and females were R=0.855 and R=0.81, respectively. The correlation coefficients between the values predicted by the weight prediction model and measured values were R=0.793 and R=0.86, respectively. Conclusion: Our chest radiographic prediction model has a high correlation with actual height and weight and can be combined with information regarding clinical nutrition factors for rapid assessment of risk for malnutrition. By training the prediction model using chest radiographs from each hospital, it can be optimized for the most common ethnic groups in the area. Importantly, our model is an illustration of the potential of automated imaging artificial intelligence for proper nutrition prediction models in older adults.

Abstract Accidental swallowing of press‐through package (PTP) sheets that could cause esophageal perforation is commonly encountered in emergency departments requiring early detection and removal. We report a case in which an accidentally swallowed PTP sheet was removed from the esophagus using a detachable snare after usual endoscopic methods proved ineffective. A Japanese woman in her 60s visited the emergency department with a persistent sore throat. Cervicothoracic computed tomography revealed presence of a PTP sheet in the cervical esophagus, and emergency endoscopy was performed. Pre‐endoscopy simulations using a sheet identical to the one swallowed by the patient indicated that the sheet would not have been retrievable using an overtube (its inner diameter was smaller than the sheet's diameter) and grasping forceps (they slipped off the sheet). It was successfully removed using a detachable snare, a device normally employed in colorectal polypectomy, allowing us to ligate the end of the sheet and pull it into the overtube. To the best of our knowledge, this is the first report of endoscopic removal of a PTP sheet from the esophagus using a detachable snare. We suggest that this novel method would facilitate removal of esophageal PTP sheets.