INVESTIGATION OF INDOOR AIR QUALITY WITH DEEP LEARNING IN HOSPITAL OPERATING ROOMS DURING COVID-19: THE CASE OF ANTALYA (Preprint)
Authors: Çoşgun Ahmet 
Published: April 2023

BACKGROUND

ABSTRACT:

The importance of indoor air quality has once again emerged all over the world with COVID-19. It is known that mortality rates due to COVID-19 increased to 2%–3% in 2021. COVID-19 is a highly contagious disease. It is transmitted by respiration or contact. Airborne particles and their quality have great importance in terms of this respiratory disease. It is also known that there is a directly proportional relationship between the quantity of pathogenic particles in the air, especially in the operating room in hospitals, and the risk of developing surgical site infection. Factors such as indoor air quality, number of particles, and number of personnel in the operating room in hospitals greatly increase the risk of surgical site infection development.
In this study, air velocity, airflow rate, ventilation rate, airflow direction, airflow characteristics, particulate matter (PM0.5, PM5), temperature, humidity, and pressure differences were measured in 4 outpatient surgery operating rooms at the Akdeniz University Faculty of Medicine Research and Training Hospital on 15-20 March 2021, during the COVID-19 period. The measurement results were added as input data to artificial neural networks using the deep learning method in MATLAB. As a result of learning and regression, the test and input data were compared. Consequently, it was observed that when parameters such as temperature, humidity, and pressure are known, the quantity of particles (PM) could be predicted by the deep learning method without any measurement in the environment. Considering the information in the current literature, this study is considered a pioneering study in the field.

OBJECTIVE

HIGHLIGHTS
•	Indoor air quality in hospital operating rooms is investigated using deep learning.
•	Airflow, temperature, pressure, and humidity characteristics are used as inputs.
•	With known temperature, humidity, and pressure, PM particle count can be predicted.
•	Deep learning can be used in particle count prediction without actual measurements.

METHODS

2.	MATERIAL AND METHOD
In this study, air velocity, airflow rate, ventilation rate, airflow direction, airflow characteristics, PM (PM0.5, PM5), temperature, humidity, and pressure difference values were measured in 4 day-case surgery operating rooms of Akdeniz University Research and Training Hospital in Antalya, Turkey, which is famous for face transplants worldwide.
The test locations and schematic of the Day-Case Surgery Operating Room of the Akdeniz University Research and Training Hospital are given in Figure 3.

In this experimental study, PM (0.5 µm, 5 µm) measurements were made in 4 operating rooms. The devices used in the measurements are described below [21].
1. Particle measurement was performed using a Ram PMSLAD-SAIR III 5100 device with the serial number 95496.
2. Airflow rate values were measured using a Testo 420 device with the serial number 50605442.
3. Air velocity and differential pressure measurements were performed using an AIM I31 310, KIMO, MPR 500 instrument with the serial number 3P161102006.
4. Clean area measurements were performed using a Skil Lazermeter Dijital device with the serial number 011847652.
5. Distances were measured using a tape measure branded Stanley Medical II/0-5 m with the serial number 144219.
6. Temperature and humidity measurements were performed using a CEM dt172 device.

2.1.	Experimental Measurement Method
Sample measurements were taken at a height of 1 m from the floor at the locations of 16 ventilation ducts in 4 operating rooms in accordance with the standards specified as TS 11605 and EN ISO 14644-1.
Spatial sample data (width, length, volume) of operating room 3 used for day-case surgeries, as well as the sizes of the HEPA filters, flow rates, flow patterns, ventilation rates, temperature, and humidity values of the room, pressure values of the room, and flow characteristics, are given in Figure 5.

Ten sample measurements taken for particle counts (PM0.5, PM5) for the area of operating room 1 are given in Figure 6.

In the volume to be measured between PM0.5 and PM5, the device was set to the relevant particle size key value (for example, after confirming the relevant selection key for dust particles with a diameter of 0.5 µm), the reading mode was then selected, and the measurement result values were read. Measurement value readings were taken instantaneously at the relevant volumes, the results were saved on the device, and the measured values were transferred to the PC.
The obtained measurement results were added as input data to artificial neural networks by the deep learning method in MATLAB. The deep learning model is shown in Figure 7.

Figure 7. Deep learning model.

As a result of learning and regression on the data, the test and input data were compared to each other. The deep learning and regression application process is shown in Figure 8.

Figure 8. Implementation of the deep learning and regression processes.

The learning performance obtained with the deep learning method is shown in Figure 9.

Figure 9. Learning performance.

The results of the method applied with the deep learning model are given in Figure 10.

Figure 10. Deep learning model results.
As a result of the method applied using the deep learning model, it was revealed that the particle count estimation could be made with 94% accuracy in the time between the periodic measurements. Figure 11 shows the indoor air quality data obtained as a result of the learning step, and Figure 12 shows the test data obtained as a result of the learning process.

Figure 11. Indoor air quality data.

Figure 12. Test data.

RESULTS

3.	DISCUSSION AND CONCLUSION
The air conditioning systems in the operating rooms of hospitals are required to not only fulfill comfort conditions but also provide hygienic conditions to the highest extent possible by ensuring that the number of particles reaches the desired standards as a result of the purifying the air in the operating rooms from particles. For this reason, the evaluation of only ventilation systems in hospital air conditioning setups is not sufficient for particle counting alone, and all data must be handled in accordance with the relevant standards in integrity for the optimum performance of air conditioning systems.
As seen in Figures 11 and 12, it was observed that the number of particles could be predicted by deep learning in MATLAB without making actual measurements in the environment as a result of considering the temperature, humidity, pressure, and measurement parameters as a whole.
In operating rooms, it is of great importance to provide air conditioning in compliance with the appropriate standards for both the patients and employees, and periodically perform performance tests. However, as it is not possible to repeat these tests every day, it was demonstrated in this study that thanks to this method, particle count estimations (e.g., PM0.5 and PM5 values) could be made with 94% accuracy with MATLAB deep learning in the time between periodic measurements.
The cost and loss of time, loss of labor, and the number of personnel necessary during measurements will be eliminated by the deep learning method implemented in MATLAB in this study for air quality estimations in day-case surgery rooms, considering that such measurements are periodically performed at locations with an ISO class greater than 5, every 12 months, as specified in Annex B of ISO 14644-1 1999.
It is thought that this study will serve as an example for other studies to be conducted with deep learning methods in MATLAB among indoor air quality studies worldwide, as well as pioneering master's and doctorate studies on this subject.

CONCLUSIONS

ACKNOWLEDGMENTS
The author would like to thank Novatest Ltd., Akdeniz University Hospital’s Chief Physician, and Akdeniz University’s Rector Prof. Özlenen Özkan for their help and contributions in the preparation of the data for this article.
FUNDING
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
CONFLICT OF INTEREST
The author does not have any conflict of interest to declare.