Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation.

Objective: Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data. Methods: We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates. The algorithm creates directories based on keywords in new file names. Results: Our work focuses on organizing computed tomography (CT), magnetic resonance (MR) images, patient clinical data, and segmented annotations. In addition, an AI model is used to generate different initial labels that can be edited manually to create ground truth labels. The manually verified ground truth labels are later included in the structured dataset using an automated algorithm for future research. Conclusion: This is a workflow with an AI model trained on local hospital medical data with output based/adapted to the users and their preferences. The automated algorithms and AI model could be implemented inside a secondary secure environment in the hospital to produce inferences.

Biochemical signs of impaired cobalamin function do not affect hematological parameters in young infants: results from a double-blind randomized controlled trial.

BACKGROUND: Whereas iron deficiency is considered the leading cause of anemia in infants, cobalamin deficiency is foremost characterized by developmental delay, and the typical macrocytic anemia is confined to severe and longstanding cobalamin deficiency in this age group. Hematological parameters were investigated in 4-mo-old infants with biochemical signs of impaired cobalamin function who participated in a randomized controlled cobalamin intervention study at 6 wk. METHODS: One hundred and seven infants were randomly assigned to receive either an intramuscular injection with 400 µg cobalamin or no intervention at 6 wk. Hematological parameters, and cobalamin and folate status were determined at inclusion and 4 mo. RESULTS: Cobalamin supplementation improved all markers of impaired cobalamin function but had no effect on hematological cell counts at 4 mo (P > 0.18). Signs indicative of an iron-restricted erythropoiesis were observed at 6 wk and 4 mo. At 4 mo, the strongest predictors of low iron status were male gender and a high percentage weight increase from birth. CONCLUSION: In infants with biochemical signs of impaired cobalamin function, supplementation does not improve hematological cell counts. Variations in erythrocyte parameters seem to be foremost associated with iron status in this age group.

Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images.

Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians. The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually. We propose anamorphic depth embedding-based lightweight CNN, called Anam-Net, to segment anomalies in COVID-19 chest CT images. The proposed Anam-Net has 7.8 times fewer parameters compared to the state-of-the-art UNet (or its variants), making it lightweight capable of providing inferences in mobile or resource constraint (point-of-care) platforms. The results from chest CT images (test cases) across different experiments showed that the proposed method could provide good Dice similarity scores for abnormal and normal regions in the lung. We have benchmarked Anam-Net with other state-of-the-art architectures, such as ENet, LEDNet, UNet++, SegNet, Attention UNet, and DeepLabV3+. The proposed Anam-Net was also deployed on embedded systems, such as Raspberry Pi 4, NVIDIA Jetson Xavier, and mobile-based Android application (CovSeg) embedded with Anam-Net to demonstrate its suitability for point-of-care platforms. The generated codes, models, and the mobile application are available for enthusiastic users at https://github.com/NaveenPaluru/Segmentation-COVID-19.