Artificial Intelligence-Based Computed Tomography Imaging Characteristics for the Diagnosis Effect of High Flow Nasal Cannula in the Treatment of Patients with Advanced Lung Cancer and Chronic Obstructive Pulmonary Disease

Abstract

Objective. This study was to compare the effects of symptomatic treatment and high flow nasal cannula (HFNC) treatment on patients with advanced lung cancer complicated with chronic obstructive pulmonary disease (COPD) and to explore the clinical application effect of HFNC treatment in such patients. Methods. 80 patients with advanced lung cancer and COPD admitted to the hospital were selected as the research objects. They were randomly divided into a control group (n = 40) and an observation group (n = 40). The computed tomography (CT) image data of all patients were classified. The neural network was trained to obtain the network weights. Based on surgery, radiotherapy, and chemotherapy, patients in the control group received anti-inflammatory, phlegm, and other symptomatic treatments, while patients in the observation group received HFNC treatment on this basis. The blood gas analysis results, clinical symptoms (cough, wheezing, rales, etc.), inflammatory factors (high-sensitivity C-reactive protein (hs-CRP), plateletcrit (PCT), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6)), and quality of life of the two groups were compared and analyzed. Results. When the model MSE was the smallest, the corresponding hidden layer neuron node value was 49, so 49 was set as the optimal number of hidden layer neuron nodes. CT images were imported into the constructed model system, and the model diagnosis system could still diagnose and classify under the premise that the pathological characteristics were not obvious. There was no significant difference in clinical data between the two groups of patients before treatment ( $P>0.05$ ). After treatment, the clinical symptoms, arterial partial pressure of carbon dioxide (PaCO2), arterial partial pressure of oxygen (PaO2), hs-CRP, PCT, TNF-α, and IL-6 levels were greatly reduced; those of patients in the observation group were much better in contrast to those of the control group ( $P<0.05$ ). The total effective rate in the observation group was 97.5%, and the effective rate in the control group was 87.5%. After treatment, the functional assessment of cancer therapy-lung (FACT-L) score was obviously higher than that before treatment ( $P<0.05$ ). After treatment, the quality of life in the observation group was increased by 45.69% compared with that before treatment, and the quality of life in the control group was increased by 35.77%. Conclusion. HFNC therapy can improve the lung function of patients with advanced lung cancer and COPD, alleviate the development of the disease, and improve the quality of life of patients.