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Microstrip isoelectric focusing with deep learning for simultaneous screening of diabetes, anemia, and thalassemia.
Fu, Haodong; Tian, Youli; Zha, Genhan; Xiao, Xuan; Zhu, Hengying; Zhang, Qiang; Yu, Changjie; Sun, Wei; Li, Chang Ming; Wei, Li; Chen, Ping; Cao, Chengxi.
Afiliação
  • Fu H; Key Laboratory of Laser Technology and Optoelectronic Functional Materials of Hainan Province, Key Laboratory of Functional Materials and Photoelectrochemistry of Haikou, College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou, 571158, PR China; School of Sensing Science and
  • Tian Y; School of Sensing Science and Engineering, SJTU-Biochine Research Center, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China; School of Materials Science and Engineering, Institute for Advanced Materials and Devices, Suzhou Universi
  • Zha G; School of Sensing Science and Engineering, SJTU-Biochine Research Center, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
  • Xiao X; NHC key Laboratory of Thalassemia Medicine, Key Laboratory of Thalassemia Medicine, Chinese Academy of Medical Sciences, Guangxi Key laboratory of Thalassemia Research, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, PR China.
  • Zhu H; NHC key Laboratory of Thalassemia Medicine, Key Laboratory of Thalassemia Medicine, Chinese Academy of Medical Sciences, Guangxi Key laboratory of Thalassemia Research, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, PR China.
  • Zhang Q; School of Sensing Science and Engineering, SJTU-Biochine Research Center, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
  • Yu C; School of Sensing Science and Engineering, SJTU-Biochine Research Center, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
  • Sun W; Key Laboratory of Laser Technology and Optoelectronic Functional Materials of Hainan Province, Key Laboratory of Functional Materials and Photoelectrochemistry of Haikou, College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou, 571158, PR China.
  • Li CM; School of Materials Science and Engineering, Institute for Advanced Materials and Devices, Suzhou University of Science and Technology, Suzhou, 215009, PR China.
  • Wei L; Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai, 200235, PR China. Electronic address: 18930173636@189.com.
  • Chen P; School of Sensing Science and Engineering, SJTU-Biochine Research Center, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China; NHC key Laboratory of Thalassemia Medicine, Key Laboratory of Thalassemia Medicine, Chinese Academy of Med
  • Cao C; School of Sensing Science and Engineering, SJTU-Biochine Research Center, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai, 200235, PR China. Electronic ad
Anal Chim Acta ; 1312: 342696, 2024 Jul 11.
Article em En | MEDLINE | ID: mdl-38834281
ABSTRACT

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, anemia, and thalassemia. Isoelectric focusing (IEF) is a common separative tool for the separation and analysis of Hb. However, the current analysis of IEF images is time-consuming and cannot be used for simultaneous screening. Therefore, an artificial intelligence (AI) of IEF image recognition is desirable for accurate, sensitive, and low-cost screening.

RESULTS:

Herein, we proposed a novel comprehensive method based on microstrip isoelectric focusing (mIEF) for detecting the relative content of Hb species. There was a good coincidence between the quantitation of Hb via a conventional automated hematology analyzer and the one via mIEF with R2 = 0.9898. Nevertheless, our results showed that the accuracy of disease diagnosis based on the quantification of Hb species alone is as low as 69.33 %, especially for the simultaneous screening of multiple diseases of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. Therefore, we introduced a ResNet1D-based diagnosis model for the improvement of screening accuracy of multiple diseases. The results showed that the proposed model could achieve a high accuracy of more than 90 % and a good sensitivity of more than 96 % for each disease, indicating the overwhelming advantage of the mIEF method combined with deep learning in contrast to the pure mIEF method.

SIGNIFICANCE:

Overall, the presented method of mIEF with deep learning enabled, for the first time, the absolute quantitative detection of Hb, relative quantitation of Hb species, and simultaneous screening of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. The AI-based diagnosis assistant system combined with mIEF, we believe, will help doctors and specialists perform fast and precise disease screening in the future.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Talassemia / Diabetes Mellitus / Aprendizado Profundo / Anemia / Focalização Isoelétrica Limite: Adult / Humans Idioma: En Revista: Anal Chim Acta Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Talassemia / Diabetes Mellitus / Aprendizado Profundo / Anemia / Focalização Isoelétrica Limite: Adult / Humans Idioma: En Revista: Anal Chim Acta Ano de publicação: 2024 Tipo de documento: Article