Deep Learning on chromatographic data for Segmentation and Sensitive Analysis

Application of artificial intelligence in cancer diagnosis and tumor nanomedicine

Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.

A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones

The traditional lateral flow immunoassay (LFIA) detection method suffers from issues such as unstable detection results and low quantitative accuracy. In this study, we propose a novel multi-test line lateral flow immunoassay quantitative detection method using smartphone-based SAA immunoassay strips. Following the utilization of image processing techniques to extract and analyze the pigments on the immunoassay strips, quantitative analysis of the detection results was conducted. Experimental setups with controlled lighting conditions in a dark box were designed to capture samples using smartphones with different specifications for analysis. The algorithm's sensitivity and robustness were validated by introducing noise to the samples, and the detection performance on immunoassay strips using different algorithms was determined. The experimental results demonstrate that the proposed lateral flow immunoassay quantitative detection method based on image processing techniques achieves an accuracy rate of 94.23% on 260 samples, which is comparable to the traditional methods but with higher stability and lower algorithm complexity.

A customizable automated container-free multi-strip detection and line recognition system for colorimetric analysis with lateral flow immunoassay for lean meat powder based on machine vision and smartphone

Ractopamine (RAC) and clenbuterol (CLE) are feed additives with adverse effects of consuming too much to food safety. It is necessary to develop an efficient and accurate colorimetric analysis method for immune-based detection of RAC and CLE. Traditional human-vision-based colorimetric analysis for lateral flow immunoassay (LFIA) is non-quantifiable and low-in-automation, while container-based and analysis-instrument-based methods are unrepeatable and high-cost. Therefore, a container-free colorimetric analysis method was developed with LFIAs image captured in dark background under smartphone flash. A multi-strip detection algorithm based on contours extraction, as well as line recognition algorithm based on grayscale projection of LFIA was developed. Finally, relative grayscale (RGS) of lines were calculated and then input into editable fitting curves to estimate concentrations. Results showed the multi-strip detection algorithm reached 98.85% and 93.70% of Recall and intersection over union (IoU), while the line recognition algorithm reached 95.07% and 97.95% of Recall and color similarity, respectively. As a result, an App was fabricated through employing LFIA of RAC and CLE, with colorimetric analysis accuracy of 98.25% and 94.50%, respectively. This study provides a container-free multi-strip colorimetric analysis method with low-cost and illumination robustness, which is a substitution for container-based and single-strip colorimetric analysis methods.

Deep learning on lateral flow immunoassay for the analysis of detection data

Lateral flow immunoassay (LFIA) is an important detection method in vitro diagnosis, which has been widely used in medical industry. It is difficult to analyze all peak shapes through classical methods due to the complexity of LFIA. Classical methods are generally some peak-finding methods, which cannot distinguish the difference between normal peak and interference or noise peak, and it is also difficult for them to find the weak peak. Here, a novel method based on deep learning was proposed, which can effectively solve these problems. The method had two steps. The first was to classify the data by a classification model and screen out double-peaks data, and second was to realize segmentation of the integral regions through an improved U-Net segmentation model. After training, the accuracy of the classification model for validation set was 99.59%, and using combined loss function (WBCE + DSC), intersection over union (IoU) value of segmentation model for validation set was 0.9680. This method was used in a hand-held fluorescence immunochromatography analyzer designed independently by our team. A Ferritin standard curve was created, and the T/C value correlated well with standard concentrations in the range of 0-500 ng/ml (R ² = 0.9986). The coefficients of variation (CVs) were ≤ 1.37%. The recovery rate ranged from 96.37 to 105.07%. Interference or noise peaks are the biggest obstacle in the use of hand-held instruments, and often lead to peak-finding errors. Due to the changeable and flexible use environment of hand-held devices, it is not convenient to provide any technical support. This method greatly reduced the failure rate of peak finding, which can reduce the customer's need for instrument technical support. This study provided a new direction for the data-processing of point-of-care testing (POCT) instruments based on LFIA.

Bionic vision autofocus method based on a liquid lens

Digital imaging systems (DISs) have been widely used in industrial process control, field monitoring, and other domains, and the autofocusing capability of DISs is a key factor affecting the imaging quality and intelligence of the system. In view of the deficiencies of focusing accuracy and speed in current imaging systems, this paper proposes a fast autofocus method of bionic vision on the basis of the liquid lens. First, the sharpness recognition network and sharpness comparison network are designed based on the consideration of a human visual focusing mechanism. Then a sharpness evaluation function combined with the distance-aware algorithm and an adaptive focusing search algorithm are proposed. These lead to the construction of our proposed autofocus method with the introduction of the memory mechanism. In order to verify the effectiveness of the proposed method, an experimental platform based on a liquid lens is built to test its performance. Experiment confirms that the proposed autofocus method has obvious advantages in robustness, accuracy, and speed compared with traditional methods.

Point-of-care biochemical assays using electrochemical technologies: approaches, applications, and opportunities

Against the backdrop of hidden symptoms of diseases and limited medical resources of their investigation, in vitro diagnosis has become a popular mode of real-time healthcare monitoring. Electrochemical biosensors have considerable potential for use in wearable products since they can consistently monitor the physiological information of the patient. This review classifies and briefly compares commonly available electrochemical biosensors and the techniques of detection used. Following this, the authors focus on recent studies and applications of various types of sensors based on a variety of methods to detect common compounds and cancer biomarkers in humans. The primary gaps in research are discussed and strategies for improvement are proposed along the dimensions of hardware and software. The work here provides new guidelines for advanced research on and a wider scope of applications of electrochemical biosensors to in vitro diagnosis.

Rapid segmentation and sensitive analysis of CRP with paper-based microfluidic device using machine learning

Microfluidic paper-based analytical devices (μPADs) have been widely used in point-of-care testing owing to their simple operation, low volume of the sample required, and the lack of the need for an external force. To obtain accurate semi-quantitative or quantitative results, μPADs need to respond to the challenges posed by differences in reaction conditions. In this paper, multi-layer μPADs are fabricated by the imprinting method for the colorimetric detection of C-reactive protein (CRP). Different lighting conditions and shooting angles of scenes are simulated in image acquisition, and the detection-related performance of μPADs is improved by using a machine learning algorithm. The You Only Look Once (YOLO) model is used to identify the areas of reaction in μPADs. This model can observe an image only once to predict the objects present in it and their locations. The YOLO model trained in this study was able to identify all the reaction areas quickly without incurring any error. These reaction areas were categorized by classification algorithms to determine the risk level of CRP concentration. Multi-layer perceptron, convolutional neural network, and residual network algorithms were used for the classification tasks, where the latter yielded the highest accuracy of 96%. It has a promising application prospect in fast recognition and analysis of μPADs.

Nanozyme enhanced magnetic immunoassay for dual-mode detection of gastrin-17

A lateral flow detection was developed for dual-mode detection of gastrin-17, including nanozyme-enhanced chromatographic detection and magnetic quantification.