Diagnosis of sickle cell anemia using AutoML on UV-Vis absorbance spectroscopy data
Sickle cell anemia is a genetic disorder that is widespread in many regions of the world. Early diagnosis through screening and preventive treatments are known to reduce mortality in the case of sickle cell disease (SCD). In addition, the screening of individuals with the largely asymptomatic condition of sickle cell trait (SCT) is necessary to curtail the genetic propagation of the disease. However, the cost and complexity of conventional diagnostic methods limit the feasibility of early diagnosis of SCD and SCT in resource-limited areas worldwide. Recently, our group developed a low-cost UV-Vis absorbance spectroscopy based diagnostic test for SCD and SCT. Here, we propose an AutoML based approach to classify the raw spectra data obtained from the developed UV-Vis spectroscopy technique with high accuracy. The proposed approach can detect the presence of sickle hemoglobin with 100% sensitivity and 93.84% specificity. This study demonstrates the potential utility of the machine learning-based absorbance spectroscopy test for deployment in mass screening programs in resource-limited settings.
Code (0)
등록된 구현이 없습니다.
Tasks
AutoMLDiagnosticSpecificitySimilar Papers 제목 키워드 기반
Diagnosis Support of Sickle Cell Anemia by Classifying Red Blood Cell Shape in Peripheral Blood Images
Red blood cell (RBC) deformation is the consequence of several diseases, including sickle cell anemia, which causes recurring episodes of pain and severe pronounced anemia. Monitoring patients with these diseases involve…
Modelling of Sickle Cell Anemia Patients Response to Hydroxyurea using Artificial Neural Networks
Hydroxyurea (HU) has been shown to be effective in alleviating the symptoms of Sickle Cell Anemia disease. While Hydroxyurea reduces the complications associated with Sickle Cell Anemia in some patients, others do not be…
Large Language Models in Ambulatory Devices for Home Health Diagnostics: A case study of Sickle Cell Anemia Management
This study investigates the potential of an ambulatory device that incorporates Large Language Models (LLMs) in cadence with other specialized ML models to assess anemia severity in sickle cell patients in real time. The…
ManagementImproving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks
Sickle cell anemia, which is characterized by abnormal erythrocyte morphology, can be detected using microscopic images. Computational techniques in medicine enhance the diagnosis and treatment efficiency. However, many …
Medical Image AnalysisA New Strategy for the Morphological and Colorimetric Recognition of Erythrocytes for the Diagnosis of Forms of Anemia based on Microscopic Color Images of Blood Smears
The detection of red blood cells based on morphology and colorimetric appearance is very important in improving hematology diagnostics. There are automatons capable of detecting certain forms, but these have limitations …