Although different FOT devices have become popular only in the recent years, the methodology of oscillation mechanics of the respiratory system was first introduced in 1956 by DuBois et al., who evaluated the airway response to pressure waves at various frequencies.^[8] Forced oscillations are induced by an external force that leads to the vibration of the system (in our case, the respiratory system). The amplitude of the oscillation depends on the frequency of the external force and the system’s natural frequency. If the external force’s frequency is close to the natural frequency, the amplitude of the oscillation will increase significantly, leading to a phenomenon known as resonance. Nowadays, machines provide portable, simple to use, and effort-independent options that are used by many clinicians. FOT is a non-invasive technique for evaluating respiratory mechanics and airway resistance during tidal breathing. The impedance of the respiratory system is measured by superimposing small-amplitude pressure oscillations on the respiratory system and measuring the resultant oscillatory flow. The pressure waves are derived from a loudspeaker, transported to the patient’s airways through a mouthpiece and the nose is obstructed with a nose clip (Fig. 2).

The patient is in a seated position with a neutral or slightly extended position of the head, the hands of the examinator or the patient himself are supporting the cheeks to decrease the oscillations in the upper airways.^[10] The test continues for 30 seconds tidal breathing and requires three to five properly executed measurements (Fig. 3).^{[11, 12]}

Main parameters derived from FOT are impedance (Z), resistance (R) and reactance (X). Impedance generalizes the relationship between pressure and airflow. The resistance (R) is the real part of Z that reflects the mechanical properties of the respiratory tree, assessing even the smallest airways. The reactance (X) is imaginary value, which indicates the elastic properties of the respiratory system. At low frequencies, it is negative. The reactance area (AX) is the area between the reactance curve and the abscissa. This is the sum of the reactance at a frequency of 5 Hz to the resonant frequency (Fres) (Fig. 4).

Medium frequencies are used usually for routine clinical application, but the implementation of both low-frequency and high-frequency forced oscillations can help uncover various mechanical properties of the respiratory system, making these techniques promising methods for lung function testing.

Some studies report that there is correlation between inflammatory changes in bronchoalveolar lavage (BAL) fluid and FOT parameters.^[26] Ramsey et al. discovered that in a study of 184 children monitored over time, an increased BAL neutrophil count was linked to a higher Fres z-score and an increase in Interleukin 8 was correlated with a lower reactance z-score at 8 Hz. However, neutrophil elastase showed no association with any of the FOT measurements.‌^[27]

Numerous studies have assessed various FOT parameters to determine their ability to identify early airway alterations in smokers.^{[28, 29]} These studies suggest that the sensitivity of FOT in identifying early airflow obstruction improves when using multiple frequencies. Lower frequencies (5 Hz) are more effective in targeting peripheral airways, while higher frequencies (20 Hz) are confined to proximal airways. Consequently, resistance and reactance at low frequencies (4–6 Hz) are particularly important for identifying smoking-related airway changes compared to higher frequencies. In cases of passive smoking, changes in R5 and R5–20 have been identified as sensitive indicators, especially in adolescents exposed to maternal smoking. Thacher et al. found in their prospective study from 2018 that maternal smoking during pregnancy was linked to the following changes in their children at 16 years of age – lower FEV₁/FVC ratio in spirometry and increased peripheral airway resistance in FOT.^[30–32]

The concept of lung function trajectories reveals that early lung diseases in infants and young children may be major risk factors for slower lung development and growth and suboptimal lung function measured in childhood.^[33] Decreased spirometry parameters and lower lung functions are predictive of later lung function and of possible risk of clinical diseases like asthma and chronic obstructive pulmonary disease (COPD) in adults.^[34]FOT is not a first-choice method in measuring lung function but contributes significantly in understanding the early lung changes in very young and non-cooperative children and this increases the strength of evidence in small airway diseases.

The diagnosis of obstructive lung diseases undergoes a severe transformation in the last decade with the development of AI. Using large datasets from diagnostic tests and efficient training algorithms AI can find a pattern, provide interpretation, and differential diagnosis for obstructive diseases. Automated interpretation of the pulmonary function tests (PFT – spirometry, bodyplethysmography and diffusion capacity test), possible with machine learning, gives promising results. The PFT, combined with new methods such as FOT, breath analysis, lung sound analysis, and computed tomography, offers a broader perspective on obstructive diseases.^[35]

Several studies have discovered machine learning techniques to detect COPD and assess its severity^{[36, 37]}, as well as early diagnosis of smoking-induced respiratory changes^[38] and bronchial asthma^[39]. The possibility of AI to predict asthma exacerbations also is encouraging, leading to decreased clinicians’ engagement in reviewing the patient’s electronic health record, improved home monitoring and more efficient asthma management.^[40] Yu et al. developed AI-based model that was able to accurately detect asthma in children at an early stage, assisting pediatricians in making correct diagnoses. This AI-driven asthma prediction model has significant clinical value and practical importance in improving the diagnosis and appropriate treatment for asthmatic children, while also reducing the misuse of related medications.^[41]

AI can be used to detect artifacts caused by coughing, swallowing, and glottis closure with pleasing success. This will decrease the examiner’s time needed to exclude breaths and artifacts and will allow the use of FOT in telemedicine and home monitoring.^[42]

The systematic analysis for the reference equations used in adult Caucasian populations around the world shows a great heterogeneity and for the preschool age there is also insufficient data. The Global Lung Function Initiative (GLI) is collecting FOT data for standardization of the method in order to transform the everyday clinical practice. The creation of reference equations for the different populations requires а proper methodology, a sufficient number of healthy controls and statistical processing. Although there are several articles that try to derive reference equations for healthy Caucasian children under the age of 5, Narchi H and AlBlooshi A^[43] focus on the role of collinearity in the statistical approach and note that in these studies, the predicted resistance varied by up to 28%. Many studies report that the main predictive factor for the results in children is height, but other factors such as age, sex, weight, and ethnicity should also be taken into account. The precision and efficiency of the method will improve with the development of appropriate reference equations to set the macro-framework of the normal values of the parameters sought.^{[15, 44-46]}

FOT is noninvasive and easy to use. It requires minimal patient cooperation, which is especially beneficial for children and elderly patients.^[13] The technique is highly sensitive in detecting early changes in the small airways. This so called “gray zone” is crucial for the early diagnosis and management of respiratory diseases.^[47]FOT provides detailed information about both airway resistance and reactance, offering a comprehensive assessment of lung function.^[15]

However, some of the lesser disadvantages of FOT are the standardization and the necessity for supplementary testing. There is a lack of standardized protocols for FOT measurements, leading to variability in results across different settings and devices.^[43] While the FOT offers valuable insights, it cannot entirely substitute traditional methods like spirometry, which remain essential for comprehensive respiratory assessment.