AIMC Topic: Hemoglobins
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Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering.
Journal of biomedical optics
Sep 6, 2024
SIGNIFICANCE: Hyperspectral cameras capture spectral information at each pixel in an image. Acquired spectra can be analyzed to estimate quantities of absorbing and scattering components, but the use of traditional fitting algorithms over megapixel i...
Deep Learning-Based Model for Non-invasive Hemoglobin Estimation via Body Parts Images: A Retrospective Analysis and a Prospective Emergency Department Study.
Journal of imaging informatics in medicine
Aug 19, 2024
Anemia is a significant global health issue, affecting over a billion people worldwide, according to the World Health Organization. Generally, the gold standard for diagnosing anemia relies on laboratory measurements of hemoglobin. To meet the need i...
Machine/deep learning-assisted hemoglobin level prediction using palpebral conjunctival images.
British journal of haematology
Jul 18, 2024
Palpebral conjunctival hue alteration is used in non-invasive screening for anaemia, whereas it is a qualitative measure. This study constructed machine/deep learning models for predicting haemoglobin values using 150 palpebral conjunctival images ta...
Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging.
BMC medical informatics and decision making
Jul 1, 2024
BACKGROUND: Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of...
Prediction of post-delivery hemoglobin levels with machine learning algorithms.
Scientific reports
Jun 17, 2024
Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data...
Spectrum-based deep learning framework for dermatological pigment analysis and simulation.
Computers in biology and medicine
Jun 15, 2024
BACKGROUND: Deep learning in dermatology presents promising tools for automated diagnosis but faces challenges, including labor-intensive ground truth preparation and a primary focus on visually identifiable features. Spectrum-based approaches offer ...
Enhancing classification accuracy of HRF signals in fNIRS using semi-supervised learning and filtering.
Progress in brain research
May 31, 2024
This paper introduces a novel approach to enhance the classification accuracy of hemodynamic response function (HRF) signals acquired through functional near-infrared spectroscopy (fNIRS). Leveraging a semi-supervised learning (SSL) framework alongsi...
Microstrip isoelectric focusing with deep learning for simultaneous screening of diabetes, anemia, and thalassemia.
Analytica chimica acta
May 6, 2024
BACKGROUND: Hemoglobin (Hb) is an important protein in red blood cells and a crucial diagnostic indicator of diseases, e.g., diabetes, thalassemia, and anemia. However, there is a rare report on methods for the simultaneous screening of diabetes, ane...
Predicting haemoglobin deferral using machine learning models: Can we use the same prediction model across countries?
Vox sanguinis
Apr 18, 2024
BACKGROUND AND OBJECTIVES: Personalized donation strategies based on haemoglobin (Hb) prediction models may reduce Hb deferrals and hence costs of donation, meanwhile improving commitment of donors. We previously found that prediction models perform ...
Impact of chronic kidney disease stages on surgical and functional outcomes in robot-assisted partial nephrectomy for localized renal tumors.
Journal of robotic surgery
Mar 5, 2024
The influence of chronic kidney disease stage on robot-assisted partial nephrectomy outcomes remains underexplored. This study aimed to assess the impact of chronic kidney disease stage on functional and surgical outcomes of robot-assisted partial ne...
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