Detection of lung injury with conventional and neural network-based analysis of continuous data

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.

Robustness of two different methods of monitoring respiratory system compliance during mechanical ventilation

Robustness measures the performance of estimation methods when they work under non-ideal conditions. We compared the robustness of artificial neural networks (ANNs) and multilinear fitting (MLF) methods in estimating respiratory system compliance (C_RS) during mechanical ventilation (MV). Twenty-four anaesthetized pigs underwent MV. Airway pressure, flow and volume were recorded at fixed intervals after the induction of acute lung injury. After consecutive mechanical breaths, an inspiratory pause (BIP) was applied in order to calculate C_RS using the interrupter technique. From the breath preceding the BIP, ANN and MLF had to compute C_RS in the presence of two types of perturbations: transient sensor disconnection (TD) and random noise (RN). Performance of the two methods was assessed according to Bland and Altman. The ANN presented a higher bias and scatter than MLF during the application of RN, except when RN was lower than 2% of peak airway pressure. During TD, MLF algorithm showed a higher bias and scatter than ANN. After the application of RN, ANN and MLF maintain a stable performance, although MLF shows better results. ANNs have a more stable performance and yield a more robust estimation of C_RS than MLF in conditions of transient sensor disconnection.

Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease

The purpose of this study is to develop a clinical decision support system based on machine learning (ML) algorithms to help the diagnostic of chronic obstructive pulmonary disease (COPD) using forced oscillation (FO) measurements. To this end, the performances of classification algorithms based on Linear Bayes Normal Classifier, K nearest neighbor (KNN), decision trees, artificial neural networks (ANN) and support vector machines (SVM) were compared in order to the search for the best classifier. Four feature selection methods were also used in order to identify a reduced set of the most relevant parameters. The available dataset consists of 7 possible input features (FO parameters) of 150 measurements made in 50 volunteers (COPD, n = 25; healthy, n = 25). The performance of the classifiers and reduced data sets were evaluated by the determination of sensitivity (Se), specificity (Sp) and area under the ROC curve (AUC). Among the studied classifiers, KNN, SVM and ANN classifiers were the most adequate, reaching values that allow a very accurate clinical diagnosis (Se > 87%, Sp > 94%, and AUC > 0.95). The use of the analysis of correlation as a ranking index of the FOT parameters, allowed us to simplify the analysis of the FOT parameters, while still maintaining a high degree of accuracy. In conclusion, the results of this study indicate that the proposed classifiers may contribute to easy the diagnostic of COPD by using forced oscillation measurements.

Automatic identification of Chronic Obstructive Pulmonary Disease Based on forced oscillation measurements and artificial neural networks

The purpose of this study is to develop an automatic classifier based on Artificial Neural Networks (ANNs) to help the diagnostic of Chronic Obstructive Pulmonary Disease (COPD) using forced oscillation measurements (FOT). The classifier inputs are the parameters provided by the FOT and the output is the indication if the parameters indicate COPD or not. The available dataset consists of 7 possible input features (FOT parameters) of 90 measurements made in 30 volunteers. Two feature selection methods (the analysis of the linear correlation and forward search) were used in order to identify a reduced set of the most relevant parameters. Two different training strategies for the ANNs were used and the performance of resulting networks were evaluated by the determination of accuracy, sensitivity (Se), specificity (Sp) and AUC. The ANN classifiers presented high accuracy (Se > 0.9, Se > 0.9 and AUC > 0.9) both in the complete and the reduce sets of FOT parameters. This indicates that ANNs classifiers may contribute to easy the diagnostic of COPD using forced oscillation measurements.

Power of breathing determined noninvasively with use of an artificial neural network in patients with respiratory failure

OBJECTIVE

To determine work of breathing per minute or power of breathing noninvasively (POB(N)) by using an artificial neural network (ANN) without the need for an esophageal catheter in patients with respiratory failure.

DESIGN

Prospective study comparing the relationship between POB(N) and invasively measured power of breathing (POB(I)).

SETTING

Intensive care unit of a university hospital.

PATIENTS

Forty-five intubated adults (age, 51 +/- 11 yrs; weight, 71 +/- 18 kg; 28 males and 17 females) receiving pressure support ventilation (PSV).

INTERVENTIONS

Data from an esophageal catheter and airway pressure/flow sensor were used to measure POB(I). A pretrained ANN provided real time calculation of POB(N). POB(I) and POB(N) were measured at various levels of PSV, ranging from 5 to 25 cm H(2)O.

MEASUREMENTS AND MAIN RESULTS

POB(N) was highly correlated with POB(I) (r = 0.91; p < .002), and because POB(N) explained or predicted 83% of the variance in POB(I), it was considered a very good predictor (r(2) = 0.83; p < .002). Bias was negligible (0.00) and precision was clinically acceptable (2.2 J/min).

CONCLUSIONS

POB can be calculated noninvasively with reasonable clinical accuracy for patients receiving ventilatory support by using an ANN. This method obviates the need for inserting an esophageal catheter and thus greatly simplifies measurement of POB. POB(N) may be a clinically useful tool for consideration when setting PSV to unload the respiratory muscles. Before considering its use in clinical practice, POB(N) would need to be incorporated within the context of load tolerance and shown to improve outcomes.

Effects of ventilatory pattern on experimental lung injury caused by high airway pressure

OBJECTIVE

To determine the influence of clinician-adjustable ventilator settings on the development of ventilator-induced lung injury, as assessed by changes in gas exchange (Pao2), compliance, functional residual capacity, and wet weight to dry weight ratio.

DESIGN

Randomized in vivo rabbit study.

SETTING

Hospital research laboratory.

SUBJECTS

Forty-four anesthetized, mechanically ventilated adult rabbits.

INTERVENTIONS

Ventilation for 2 hrs with pressure control ventilation at 45 cm H2O, Fio2 = 0.6, and randomization to one of five ventilatory strategies using combinations of positive end-expiratory pressure (3 or 12 cm H2O), inspiratory time (0.45, 1.0, or 2.0 secs), and frequency (9 or 23/min).

MEASUREMENTS AND MAIN RESULTS

Among the ventilator strategies applied, PEEP at 12 cm H2O (elevated positive end-expiratory pressure) and inspiratory time at 0.45 secs (reduced inspiratory time) best preserved Pao2 (p <.003) and compliance (p <.035). During injury development, two consistent changes were observed: Tidal volume increased, and airway pressure waveform was transformed by extending the time to attain target pressure.

CONCLUSIONS

In this preclinical model, lung injury was attenuated by decreasing inspiratory time. As lung injury occurred, tidal volume increased and airway pressure waveform changed.

Estimating respiratory system compliance during mechanical ventilation using artificial neural networks

In this study we evaluated whether a technology based on artificial neural networks (ANN) could estimate the static compliance (C(RS)) of the respiratory system, even in the absence of an end-inspiratory pause, during continuous mechanical ventilation. A porcine model of acute lung injury was used to provide recordings of different respiratory mechanics conditions. Each recording consisted of 10 or more consecutive breaths in volume-controlled mechanical ventilation, followed by a breath having an end-inspiratory pause used to calculate C(RS) according to the interrupter technique (IT). The volume-pressure loop of the breath immediately preceding the one with pause was given to the ANN for the training, together with the C(RS) separately calculated by the IT. The prospective phase consisted of giving only the loops to the trained ANN and comparing the results yielded by it to the compliance separately calculated by the investigators. Determination of measurement agreement between ANN and IT methods showed an error of -0.67 +/- 1.52 mL/cm H(2)O (bias +/- SD). We could conclude that ANN, during volume-controlled mechanical ventilation, can extract C(RS) without needing to stop inspiratory flow.

IMPLICATIONS

We studied the application of artificial neural networks (ANN) to the estimation of respiratory compliance during mechanical ventilation. The study was performed on an animal model of acute lung injury, testing the performance of ANN in both healthy and diseased conditions of the lung.

Assessment of respiratory system mechanics by artificial neural networks: an exploratory study

We evaluated 1) the performance of an artificial neural network (ANN)-based technology in assessing the respiratory system resistance (Rrs) and compliance (Crs) in a porcine model of acute lung injury and 2) the possibility of using, for ANN training, signals coming from an electrical analog (EA) of the lung. Two differently experienced ANNs were compared. One ANN (ANN(BIO)) was trained on tracings recorded at different time points after the administration of oleic acid in 10 anesthetized and paralyzed pigs during constant-flow mechanical ventilation. A second ANN (ANN(MOD)) was trained on EA simulations. Both ANNs were evaluated prospectively on data coming from four different pigs. Linear regression between ANN output and manually computed mechanics showed a regression coefficient (R) of 0.98 for both ANNs in assessing Crs. On Rrs, ANN(BIO) showed a performance expressed by R = 0.40 and ANN(MOD) by R = 0.61. These results suggest that ANNs can learn to assess the respiratory system mechanics during mechanical ventilation but that the assessment of resistance and compliance by ANNs may require different approaches.