Detecting Left Heart Failure in Echocardiography through Machine Learning: A Systematic Review.

Background: Heart failure remains a considerable burden to healthcare in Asia. Early intervention, mainly using echocardiography, to assess cardiac function is crucial. However, due to limited resources and time, the procedure has become more challenging during the COVID-19 pandemic. On the other hand, studies have shown that artificial intelligence (AI) is highly potential in complementing the work of clinicians to diagnose heart failure accurately and rapidly. Methods: We systematically searched Europe PMC, ProQuest, Science Direct, PubMed, and IEEE following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and our inclusion and exclusion criteria. The 14 selected works of literature were then assessed for their quality and risk of bias using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies). Results: A total of 2105 studies were retrieved, and 14 were included in the analysis. Five studies posed risks of bias. Nearly all studies included datasets in the form of 3D (three dimensional) or 2D (two dimensional) images, along with apical four-chamber (A4C) and apical two-chamber (A2C) being the most common echocardiography views used. The machine learning algorithm for each study differs, with the convolutional neural network as the most common method used. The accuracy varies from 57% to 99.3%. Conclusions: To conclude, current evidence suggests that the application of AI leads to a better and faster diagnosis of left heart failure through echocardiography. However, the presence of clinicians is still irreplaceable during diagnostic processes and overall clinical care; thus, AI only serves as complementary assistance for clinicians.

Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography.

BACKGROUND: The accuracy of an artificial intelligence model based on echocardiography video data in the diagnosis of heart failure (HF) called LIFES (Learning Intelligent for Effective Sonography) was investigated. METHODS: A cross-sectional diagnostic test was conducted using consecutive sampling of HF and normal patients' echocardiography data. The gold-standard comparison was HF diagnosis established by expert cardiologists based on clinical data and echocardiography. After pre-processing, the AI model is built based on Long-Short Term Memory (LSTM) using independent variable estimation and video classification techniques. The model will classify the echocardiography video data into normal and heart failure category. Statistical analysis was carried out to calculate the value of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratio (LR). RESULTS: A total of 138 patients with HF admitted to Harapan Kita National Heart Center from January 2020 to October 2021 were selected as research subjects. The first scenario yielded decent diagnostic performance for distinguishing between heart failure and normal patients. In this model, the overall diagnostic accuracy of A2C, A4C, PLAX-view were 92,96%, 90,62% and 88,28%, respectively. The automated ML-derived approach had the best overall performance using the 2AC view, with a misclassification rate of only 7,04%. CONCLUSION: The LIFES model was feasible, accurate, and quick in distinguishing between heart failure and normal patients through series of echocardiography images.

Dataset of vehicle images for Indonesia toll road tariff classification.

Vehicle classifications with different methods have been applied for many purposes. The data provided in this article is useful for classifying vehicle purposes following the Indonesia toll road tariffs. Indonesia toll road tariff regulations divide vehicles into five groups as follows, group-1, group-2, group-3, group-4, and group-5, respectively. Group-1 is a class of non-truck vehicles, while group-2 to group-5 are classes of truck vehicles. The non-truck class consists of the sedan, pick-up, minibus, bus, MPV, and SUV. Truck classes are grouped based on the number of truck's axles. Group-2 is a class of trucks with two axles, a group-3 truck with three axles, a group-4 truck with four axles, and a group-5 truck with five axles or more. The dataset is categorized into five classes accordingly, which are group-1, group-2, group-3, group-4, and group-5 images. The data made available in this article observes images of vehicles obtained using a smartphone camera. The vehicle images dataset incorporated with deep learning, transfer learning, fine-tuning, and the Residual Neural Network (ResNet) model can yield exceptional results in the classification of vehicles by the number of axles.

Dataset of short-term prediction of CO2 concentration based on a wireless sensor network.

This CO2 data is gathered from WSN (Wireless Sensor Network) sensors that is placed in some areas. To make this observation framework run effectively, examining the relationships between factors is required. We can utilize multiple wireless sensor devices. There are three parts of the system, including the sensor device, the sink node device, and the server. We use those devices to acquire data over a three-month period. In terms of the server infrastructure, we utilized an application server, a user interface server, and a database server to store our data. This study built a WSN framework for CO2 observations. We investigate, analyze, and predict the level of CO2, and the results have been collected. The Random Forest algorithm achieved a 0.82 R2 Score.

Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system.

The human sensory test is often used for obtaining the sensory quantities of odors, however, the fluctuation of results due to the expert's condition can cause discrepancies among panelists. Authors have studied the artificial odor discrimination system using a quartz resonator sensor and a back-propagation neural network as the recognition system, however, the unknown category of odor is always recognized as the known category of odor. In this paper, a kind of fuzzy algorithm for learning vector quantization (LVQ) is developed and used as a pattern classifier. In this type of fuzzy LVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistics of the measurement error directly. During learning, the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated by shifting the central position of the fuzzy reference vector toward or away from the input vector, and by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of fuzzy-neuro (FN) LVQ is different in nature from fuzzy algorithm (FA) LVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in an artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ could provide high recognition probability in determining various known categories of odors, however, the FNLVQ neural system has the ability to recognize the unknown category of odor that could not be recognized by the FALVQ neural system.