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  1. Nazarudin AA, Zulkarnain N, Mokri SS, Zaki WMDW, Hussain A, Ahmad MF, et al.
    Diagnostics (Basel), 2023 Feb 16;13(4).
    PMID: 36832237 DOI: 10.3390/diagnostics13040750
    Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu's thresholding with the Chan-Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu's thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan-Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan-Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan-Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan-Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu's thresholding and the Chan-Vese method enhanced the segmentation of ultrasound images.
  2. Mustafar R, Hishamuddin KAM, Mohd R, Kamaruzaman L, Halim WHWA, Hsien YM, et al.
    BMC Nephrol, 2023 Nov 13;24(1):338.
    PMID: 37957551 DOI: 10.1186/s12882-023-03386-w
    BACKGROUND: The prevalence of chronic kidney disease (CKD) is rising in Malaysia. Early detection is necessary to prevent disease progression, especially in terms of cardiovascular (CV) risk, the main cause of death in end-stage renal disease (ESRD). Retinal changes have proven to be a good predictor of CKD whereas cardiac biomarkers are useful in cardiovascular risk stratification. We aimed to demonstrate the correlation between retinal changes and cardiac biomarkers with CKD.

    METHODS: This single-centre cross-sectional study was conducted among patients with CKD stages 3, 4, and 5 (not on dialysis) from the Nephrology Clinic, Universiti Kebangsaan Malaysia Medical Centre. A total of 84 patients were recruited with an even distribution across all three stages. They underwent fundus photography where images were analysed for vessel calibre (central retinal venular equivalent (CRVE), central retinal arterial equivalent (CRAE), and tortuosity indices. Optical coherence tomography was used to measure macular volume. Blood samples were sent for laboratory measurement of high-sensitivity C-reactive protein (hs-CRP) and asymmetric dimethylarginine (ADMA). These parameters were analysed in relation to CKD.

    RESULTS: The mean age was 58.8 ± 11.7 years, with 52.4% male and 47.6% female patients. Among them, 64.3% were diabetics. Retinal vessel tortuosity (r = -0.220, p-value = 0.044) had a negative correlation with the estimated glomerular filtration rate (eGFR). CRVE showed a positive correlation with proteinuria (r = 0.342, p = 0.001) but negative correlation with eGFR (r = -0.236, p = 0.031). Hs-CRP positively correlated with proteinuria (r = 0.313, p = 0.04) and negatively correlated with eGFR (r = -0.370, p = 0.001). Diabetic patients had a higher CRVE compared to non-diabetic patients (p = 0.02). History of ischaemic heart disease was associated with a smaller macula volume (p = 0.038). Male gender (r2 = 0.066, p = 0.031) and HbA1c had a positive influence (r2 = 0.066, p = 0.047) on retinal vessel tortuosity. There was a positive influence of age (r2 = 0.183, p = 0.012) and hs-CRP (r2 = 0.183, p = 0.045) on CRVE. As for macula volume, it negatively correlated with diabetes (r2 = 0.015, p = 0.040) and positively correlated with smoking (r2 = 0.015, p = 0.012).

    CONCLUSION: Our study showed that eGFR value affects retinal vessel tortuosity, CRVE and hs-CRP. These parameters bear potential to be used as non-invasive tools in assessing CKD. However, only macula volume may be associated with CVD risk among the CKD population.

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