Abstract. This paper focuses on the improvement of the diagnostic accuracy of focal liver lesions by quantifying the key features of cysts, hemangiomas, and malignant lesions on ultrasound images. The focal liver lesions were divided into 29 cysts, 37 hemangiomas, and 33 malignancies. A total of 42 hybrid textural features that composed of 5 first order statistics, 18 gray level co-occurrence matrices, 18 Law's, and echogenicity were extracted. A total of 29 key features that were selected by principal component analysis were used as a set of inputs for a feed-forward neural network. For each lesion, the performance of the diagnosis was evaluated by using the positive predictive value, negative predictive value, sensitivity, specificity, and accuracy. The results of the experiment indicate that the proposed method exhibits great performance, a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst vs. malignant, and hemangioma vs. malignant) on ultrasound images. The accuracy was slightly increased when echogenicity was included in the optimal feature set. These results indicate that it is possible for the proposed method to be applied clinically.

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Hydrophobic acrylic intraocular lens (IOL) is the most popular material in cataract surgery. Posterior capsule opacification (PCO) is a long-term complication of cataract surgery. It can impair vision and adversely affect the prognosis of IOL delamination. The objective of this study was to perform a systematic review and meta-analysis to provide an updated evaluation of long-term complications and visual function after implantation with hydrophobic acrylic and silicone intraocular lenses. PubMed, Embase, and Cochrane Library were searched from January 2000 until March 2021. Randomized controlled trials (RCTs) and retrospective studies were finally included. The main outcomes were PCO value and neodymium-doped yttrium aluminum garnet (Nd : YAG) capsulotomy rate. Subgroup analysis was performed to compare hydrophobic acrylic and silicone IOLs during the follow-up period. Sensitivity analysis was also performed. The meta-analysis included a total of 17 studies. When the follow-up period was considered, the results of the analysis revealed higher PCO value (Group 3: standardized mean difference (SMD), −0.59; 95% confidence interval (CI), −0.90 to −0.28) and Nd : YAG capsulotomy rate (Group 3: risk ratio (RR), 0.60; 95% CI, 0.40–0.89) for hydrophobic acrylic IOLs than silicone IOLs during a long-term (≥6 years) follow-up. In conclusion, both the PCO value and the Nd : YAG capsulotomy rates were higher in hydrophobic acrylic IOLs group than the silicone IOLs group at long-term use (more than 6 years) after implantation.

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BackgroundIntravascular ultrasound (IVUS) is a commonly used diagnostic imaging method for coronary artery disease. Virtual histology (VH) characterizes the plaque components into fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), or dense calcium (DC). However, VH can obtain only a single-frame image in one cardiac cycle, and specific software is needed to obtain the radio frequency data. This study proposed a novel intensity-based multi-level classification model for plaque characterization.MethodsThe plaque-containing regions between the intima and the media-adventitia were segmented manually for all IVUS frames. A total of 54 features including first order statistics, grey level co-occurrence matrix, Law’s energy measures, extended grey level run length matrix, intensity, and local binary pattern were estimated from the plaque-containing regions. After feature extraction, optimal features were selected using principle component analysis (PCA), and these were utilized as the input for the classification models. Plaque components were classified into FT, FFT, NC, or DC using an intensity-based multi-level classification model consisting of three different nets. Net 1 differentiated low-intensity components into FT/FFT and NC/DC groups. Then, net 2 subsequently divided FT/FFT into FT or FFT, whereas the remainder and high-intensity components were classified into NC or DC via net 3. To improve classification accuracy, each net utilized three different input features obtained by PCA. Classification performance was evaluated in terms of sensitivity, specificity, accuracy, and receiver operating characteristic curve.ResultsQuantitative results indicated that the proposed method showed significantly high classification accuracy for all tissue types. The classifiers had classification accuracies of 85.1%, 71.9%, and 77.2%, respectively, and the areas under the curve were 0.845, 0.704, and 0.783. In particular, the proposed method achieved relatively high sensitivity (82.0%) and specificity (87.1%) for differentiating between the FT/FFT and NC/DC groups.ConclusionsThese results confirmed the clinical applicability of the proposed approach for IVUS-based tissue characterization.