Curve-Modelling and Machine Learning for a Better COPD Diagnosis.

Background: Development of new tools in artificial intelligence has an outstanding performance in the recognition of multidimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD). Methods: This was an observational analytical single-centre study in patients with spirometry performed in outpatient medical care. The segment that goes from the peak expiratory flow to the forced vital capacity was modelled with quadratic polynomials, the coefficients obtained were used to train and test neural networks in the task of classifying patients with COPD. Results: A total of 695 patient records were included in the analysis. The COPD group was significantly older than the No COPD group. The pre-bronchodilator (Pre BD) and post-bronchodilator (Post BD) spirometric curves were modelled with a quadratic polynomial, and the coefficients obtained were used to feed three neural networks (Pre BD, Post BD and all coefficients). The best neural network was the one that used the post-bronchodilator coefficients, which has an input layer of 3 neurons and three hidden layers with sigmoid activation function and two neurons in the output layer with softmax activation function. This system had an accuracy of 92.9% accuracy, a sensitivity of 88.2% and a specificity of 94.3% when assessed using expert judgment as the reference test. It also showed better performance than the current gold standard, especially in specificity and negative predictive value. Conclusion: Artificial Neural Networks fed with coefficients obtained from quadratic and cubic polynomials have interesting potential of emulating the clinical diagnostic process and can become an important aid in primary care to help diagnose COPD in an early stage.

The Challenges of Spirometric Diagnosis of COPD.

Chronic obstructive pulmonary disease (COPD) is one of the top causes of morbidity and mortality worldwide. Although for many years its accurate diagnosis has been a focus of intense research, it is still challenging. Due to its simplicity, portability, and low cost, spirometry has been established as the main tool to detect this condition, but its flawed performance makes it an imperfect COPD diagnosis gold standard. This review aims to provide an up-to-date literature overview of recent studies regarding COPD diagnosis; we seek to identify their limitations and establish perspectives for spirometric diagnosis of COPD in the XXI century by combining deep clinical knowledge of the disease with advanced computer analysis techniques.

Comparison of New Spirometry Measures to Diagnose COPD.

BACKGROUND: COPD is diagnosed by using FEV1/FVC, which has limitations as a diagnostic test. We assessed the validity of several measures derived from the expiratory phase of the flow-volume curve obtained from spirometry to diagnose COPD: the slopes that correspond to the volume expired after the 50% and 75% of the FVC, the slope formed between the peak expiratory flow (PEF) and the FVC, and the area under the expiratory flow/volume curve. METHODS: We conducted a cross-sectional diagnostic test study in 765 consecutive subjects referred for spirometry because of respiratory symptoms. We compared the reproducibility and accuracy of the proposed measures against post-bronchodilator FEV1/FVC < 0.70. We also evaluated the proportion of respiratory symptoms for the FEV1/FVC, FEV1 per FEV in the first 6 s (FEV6), and the PEF slope. RESULTS: The subjects had a mean age of 65.8 y, 57% were women, and 35% had COPD. The test-retest intraclass correlation coefficient values were 0.89, 0.85, and 0.83 for FEV1/FVC, FEV1/FEV6, and the PEF slope, respectively. The area under the curve values were 0.93 (expiratory flow/volume), 0.96 (potential expiratory flow/volume), 0.97 (potential expiratory flow/volume at 75% of FVC), and 0.82 (potential expiratory flow/volume at 50% of FVC). The area under the receiver operating characteristic curve was 0.99 for FEV1/FEV6, 0.99 for the slope at 50% of the FVC, and 0.98 for the PEF slope. CONCLUSIONS: The FEV1/FEV6, PEF slope, and 50% FVC slopes had similar diagnostic performances compared with FEV1/FVC.