Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis

Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis

This study presents a groundbreaking approach for the early detection of chronic kidney disease (CKD) and other urological disorders through an image-label-free, multi-dipstick identification method, eliminating the need for complex machinery, label libraries, or preset coordinates. Our research successfully identified reaction pads on 187 multi-dipsticks, each with 11 pads, leveraging machine learning algorithms trained on human urine data. This technique aims to surpass traditional colourimetric methods and concentration-colour curve fitting, offering more robust and precise community screening and home monitoring capabilities. The developed algorithms enhance the generalizability of machine learning models by extracting primary colours and correcting urine colours on each reaction pad. This method's cost-effectiveness and portability are significant, as it requires no additional equipment beyond a standard smartphone. The system's performance rivals professional medical equipment without auxiliary lighting or flash under regular indoor light conditions, effectively managing false positives and negatives across various categories with remarkable accuracy. In a controlled experimental setting, we found that random forest algorithms, based on a Bagging strategy and applied in the HSV colour space, showed optimal results in smartphone-assisted urinalysis. This study also introduces a novel urine colour correction method, significantly improving machine learning model performance. Additionally, ISO parameters were identified as crucial factors influencing the accuracy of smartphone-based urinalysis in the absence of additional lighting or optical configurations, highlighting the potential of this technology in low-resource settings.

Topic prediction for tobacco control based on COP9 tweets using machine learning techniques

The prediction of tweets associated with specific topics offers the potential to automatically focus on and understand online discussions surrounding these issues. This paper introduces a comprehensive approach that centers on the topic of "harm reduction" within the broader context of tobacco control. The study leveraged tweets from the period surrounding the ninth Conference of the Parties to review the Framework Convention on Tobacco Control (COP9) as a case study to pilot this approach. By using Latent Dirichlet Allocation (LDA)-based topic modeling, the study successfully categorized tweets related to harm reduction. Subsequently, various machine learning techniques were employed to predict these topics, achieving a prediction accuracy of 91.87% using the Random Forest algorithm. Additionally, the study explored correlations between retweets and sentiment scores. It also conducted a toxicity analysis to understand the extent to which online conversations lacked neutrality. Understanding the topics, sentiment, and toxicity of Twitter data is crucial for identifying public opinion and its formation. By specifically focusing on the topic of "harm reduction" in tweets related to COP9, the findings offer valuable insights into online discussions surrounding tobacco control. This understanding can aid policymakers in effectively informing the public and garnering public support, ultimately contributing to the successful implementation of tobacco control policies.

Simultaneous uroflowmetry and urinalysis with single specimen - A prospective evaluation of automatic urine strip analyzer of ORUBA INALYS: Uroflowmetry-urinalysis combined device

Objective

To evaluate the consistency of the urinalysis results performed with the ORUBA INALYS device, (Oruba, Ankara, Turkey) which can perform urinalysis and uroflowmetry simultaneously, with the analysis results performed with the SYSMEX UC3500 automated urine chemistry analyzer (Sysmex, Kobe, Japan).

Material and methods

In this prospective study, urinalysis of 50 male patients with lower urinary tract symptoms were evaluated. The parameters of glucose, pH, urobilinogen, bilirubin and ketone, leukocyte, protein, and blood were measured with ORUBA INALYS, and the same urine specimens collected from ORUBA INALYS by a special setup were sent to the laboratory for urinalysis with Sysmex UC-3500 to assess the concordance of the results between two devices.

Results

Urinalysis results in ORUBA INALYS device in terms of glucose, pH, urobilinogen, bilirubin, and ketone parameters were shown to achieve 100% agreement within ±1 category with SYSMEX UC3500 whereas these values were slightly decreased to 88%, 96%, and 98% for leukocyte, protein, and blood, respectively. Among the calculable weighted kappa values for the test parameters, the highest value was found for glucose and followed by blood, pH, leukocyte, and specific gravity respectively.

Conclusion

Significant consistency of the urinalysis results obtained from ORUBA INALYS with those obtained from device SYSMEX UC3500 shows the reliability of the urinalysis performed with ORUBA INALYS. ORUBA INALYS could minimize costs and workload, provide time save and reduce plastic waste.

Fast Control for Backlight Power-Saving Algorithm Using Motion Vectors from the Decoded Video Stream

Backlight power-saving algorithms can reduce the power consumption of the display by adjusting the frame pixels with optimal clipping points under some tradeoff criteria. However, the computation for the selected clipping points can be complex. In this paper, a novel algorithm is created to reduce the computation time of the state-of-the-art backlight power-saving algorithms. If the current frame is similar to the previous frame, it is unnecessary to execute the backlight power-saving algorithm for the optimal clipping points, and the derived clipping point from the previous frame can be used for the current frame automatically. In this paper, the motion vector information was used as the measurement of the similarity between adjacent frames, where the generation of the motion vector information requires no extra complexity since it is generated to reconstruct the decoded frame pixels before the display. The experiments showed that the proposed work can reduce the running time of the state-of-the-art methods by 25.21% to 64.22%, while the performances are maintained; the differences with the state-of-the-art methods in PSNR are only 0.02~1.91 dB, and those in power are only -0.001~0.008 W.