Objective:To develop an artificial intelligence-based model for automated recognition of serum indices using machine vision and deep learning.Methods:This study was a cross-sectional study.Serum sample images were collected fromWest China Hospital of Sichuan University from September 21, 2020 to January 20, 2023 using the imaging device of the fully automated sample pre-processing system. A computer random number generator was used to randomly select one whole hour each day, and all serum sample images processed within that hour were included. After excluding samples with unqualified images and missing serum index results, a total of 5, 534 samples were included. These were divided into a training set and a test set in an 8∶2 ratio using Python random shuffle function, and 4, 458 samples were in the training set and 1, 076 samples were in the test set. After manual inspection, the serum regions were annotated using the MATLAB Image Labeler tool and converted into YOLO format, and a YOLO v5-based model was constructed for automatic serum region extraction. The actual values of lipemia index (L-index), hemolysis index (H-index), and icterus index (I-index) were measured using the automatic biochemical analyzerwith matched reagent kits. A serum index regression model was constructed based on the MobileNet v2 network using the PyTorch 1.10.0 framework. The grading performance of the model was evaluated using accuracy, Kappa coefficient, sensitivity, specificity, positive predictive value, and negative predictive value. Regression performance was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and Bland-Altman analysis.Results:The overall accuracy rates for grading L-index, H-index, and I-index were 98.88%, 95.26%, and 92.47%, respectively, with Kappa coefficients of 0.72, 0.72, and 0.59. For L-index, MAE was 5.11, RMSE was 9.77, and R2 was 0.78. For H-index, MAE was 5.18, RMSE was 8.99, and R2 was 0.89. For I-index, MAE was 1.13, RMSE was 3.01, and R2 was 0.71. Bland-Altman analysis showed that 95.5%, 95.1%, and 95.7% of the data points fell within the consistency intervals for L-index, H-index, and I-index, respectively.Conclusion:The study developed an artificial intelligence-based serum index regression modelto estimate serum indices with high efficiency and accuracy. It shows great potential for reducing laboratory costs, improving clinical testing efficiency, and promoting intelligent development in laboratory medicine.

Objective:To develop an artificial intelligence-based model for automated recognition of serum indices using machine vision and deep learning.Methods:This study was a cross-sectional study.Serum sample images were collected fromWest China Hospital of Sichuan University from September 21, 2020 to January 20, 2023 using the imaging device of the fully automated sample pre-processing system. A computer random number generator was used to randomly select one whole hour each day, and all serum sample images processed within that hour were included. After excluding samples with unqualified images and missing serum index results, a total of 5, 534 samples were included. These were divided into a training set and a test set in an 8∶2 ratio using Python random shuffle function, and 4, 458 samples were in the training set and 1, 076 samples were in the test set. After manual inspection, the serum regions were annotated using the MATLAB Image Labeler tool and converted into YOLO format, and a YOLO v5-based model was constructed for automatic serum region extraction. The actual values of lipemia index (L-index), hemolysis index (H-index), and icterus index (I-index) were measured using the automatic biochemical analyzerwith matched reagent kits. A serum index regression model was constructed based on the MobileNet v2 network using the PyTorch 1.10.0 framework. The grading performance of the model was evaluated using accuracy, Kappa coefficient, sensitivity, specificity, positive predictive value, and negative predictive value. Regression performance was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and Bland-Altman analysis.Results:The overall accuracy rates for grading L-index, H-index, and I-index were 98.88%, 95.26%, and 92.47%, respectively, with Kappa coefficients of 0.72, 0.72, and 0.59. For L-index, MAE was 5.11, RMSE was 9.77, and R2 was 0.78. For H-index, MAE was 5.18, RMSE was 8.99, and R2 was 0.89. For I-index, MAE was 1.13, RMSE was 3.01, and R2 was 0.71. Bland-Altman analysis showed that 95.5%, 95.1%, and 95.7% of the data points fell within the consistency intervals for L-index, H-index, and I-index, respectively.Conclusion:The study developed an artificial intelligence-based serum index regression modelto estimate serum indices with high efficiency and accuracy. It shows great potential for reducing laboratory costs, improving clinical testing efficiency, and promoting intelligent development in laboratory medicine.

Tongue squamous cell carcinoma is highly malignant and has a poor prognosis. In this study, we aimed to combine whole-genome sequencing, whole-genome methylation, and whole-transcriptome analyses to understand the molecular mechanisms of tongue squamous cell carcinoma better. Oral tongue squamous cell carcinoma and adjacent normal tissues from five patients with tongue squamous cell carcinoma were included as five paired samples. After multi-omics sequencing, differentially methylated intervals, methylated loop sites, methylated promoters, and transcripts were screened for variation in all paired samples. Correlations were analyzed to determine biological processes in tongue squamous cell carcinoma. We found five mutated methylation promoters that were significantly associated with mRNA and lncRNA expression levels. Functional annotation of these transcripts revealed their involvement in triggering the mitogen-activated protein kinase cascade, which is associated with cancer progression and the development of drug resistance during treatment. The prognostic signature models constructed based on WDR81 and HNRNPH1 and combined clinical phenotype-gene prognostic signature models showed high predictive efficacy and can be applied to predict patient prognostic risk in clinical settings. We identified biological processes in tongue squamous cell carcinoma that are initiated by mutations in the methylation promoter and are associated with the expression levels of specific mRNAs and lncRNAs. Collectively, changes in transcript levels affect the prognosis of tongue squamous cell carcinoma patients.
Humans ; Biomarkers, Tumor ; Nerve Tissue Proteins ; Prognosis ; Squamous Cell Carcinoma of Head and Neck/pathology* ; Tongue Neoplasms/pathology*