JOURNAL OF

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JOURNAL OF THE INTERNATIONAL SOCIETY FOR TELEMEDICINE AND EHEALTH

A WEB OF IOT SENSORS TO AUTOMATE QUALITY CONTROL IN AN IVF EMBRYOLOGY LAB

Maninder Sra MSc, Kaninka Panda MD, Satish Rath MBBS, MD

Santaan Fertility Clinic and Research Institute, Bhubaneswar, India

Abstract

IOT (Internet of Things) sensors have been very effective in real-time monitoring of an environment. With currently available low- maintenance sensors able to perform accurately for months on end, there is an opportunity to use this technology for sensitive monitoring of high performance lab environments, such as an in vitro fertilisation (IVF) lab. This paper is an attempt to use a network of well positioned IOT sensors to measure key parameters such as temperature, humidity, door-open count and volatile organic compounds (VOC) content inside an IVF lab non- stop for a stretch of one month and seek new insights from the resulting data in real-time.

Keywords: real-time sensing; eHealth; quality control;

data analytics; IOT

Introduction

IOT is the concept of Internet-of-things involving sensors which can communicate data to Internet for real-time data insights from the sensor device about the monitored environment. This can be very helpful in quality control situations, especially monitoring lab conditions for performance based labs such as in vitro fertilisation (IVF) where the correct temperature, humidity and volatile organic compounds (VOC) content present are critical to human embryo growth.

The IVF lab essentially takes over the role of a mother’s womb in terms of being the environment for the growing embryo. So once out of the incubator the embryo has to be maintained in optimum condition at all times. It is specifically susceptible to temperature, pH and VOC content of the room. Air is recirculated in an IVF lab, so maintaining air quality of this recirculating air is a task requiring regular attention and monitoring. Moreover, oocyte retrieval and embryo transfer, which demands cooperation with the adjacent operating theatre, creates a challenge for maintaining clean air conditions.¹

Air quality consistency inside an IVF lab is of paramount importance and still a challenge for labs even with no constraint on resources at their disposal.² In a busy urban area, air contains vehicle pollutants (carbon monoxide, nitrous oxide, sulphur dioxide, heavy metals), pesticides, nearby cooking oil residues, high dust content and for a lab, more than 20 types of VOC particles, especially at the beginning of lab setup.³ Any of these can adversely affect the quality of a very sensitive embryo.

The air quality of a lab involves gradual changes in the parameters over time and is dependent on outside temperature changes at various times of the day from morning to night. Also the frequency of entry into such protected spaces can unavoidably introduce the outer air elements. To maintain such room environment requires real-time monitoring backed with triggers to check back the situation in time enough to prevent any harsh exposure to the embryos once they are outside the incubator for lab procedure.

The aim of this study was to enable real-time quality monitoring of an IVF lab by way of network of IOT sensors to maintain critical parameters like temperature, humidity, VOC content, door opening duration in a working range.

Methods

IOT sensors were deployed at the IVF Lab of the Santaan Fertility Centre and Research Institute to detect temperature, humidity, VOC, vibration and lab entry. Real-time data was received online using the platform Thingspeak, with mobile support to provide triggers to alert for deviations.

Results

The results, in terms of live data, conveyed the trends in the lab for the above parameters, giving crucial insights on the levels of VOC, temperature and humidity at various times of the day and the relation between door opening and VOC levels, to manage the

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lab better. One month’s continuous data (December, 2016) was monitored at 20 s intervals of 20s, with an average VOC content of 0.0042 ppm (average of

121166 sensor data

values in total), an average temperature of 26.4337°C average of 118824 sensor data values in total).

Examples of data output are shown in Figures 1-6.

Figure 1. Four minute real-time display as seen online anywhere for remote monitoring and accounting of the lab. (Platform support: Thingspeak.com)

Figure 2. One month’s VOC data showing consistent values maintained in the lab for operations (x axis- sensor data values at 20s intervals, y axis- VOC content in ppm).

Figure 3. Another view of VOC for showing an increase in value over a period of few hours at the time of lab activity (x axis-

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Figure 4. Two day temperature values totalling 8000 points spread at 20s intervals. As per the graph the average temperature maintained is 24°C the anomalies seen here as spikes are due to inconsistencies in the sensor performance (thick casing) which was rectified by replacement. Graphs created through: Plotly¹

Figure 5. An overview of the month long lab-temperature profile (x axis- sensor data points).

Figure 6. Instances where the door was opened for a particular duration over a period of about 3 months. (y axis- time is seconds when door open, x axis- sensor data points)

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We found door opening instances can be a very good indicator of lab environment sensitivity to outside elements.

Discussion

The present setup explores critical parameter variations in real-time. Deviations can be traced back in time, accurate to 20s intervals, for comparison to subsequent values of other sensors to get a complete snapshot of the lab at specific date and time to troubleshoot lapses in lab performance or for general maintenance status. The setup enabled the understanding of the impact of various actions in the lab on air quality for better future quality- management.

A dynamic air quality map. Sensors were tried at various lab locations to best measure variations in air temperature and VOC. Having multiple sensors for these parameters gives unique insights about the air flow patterns inside the lab and how they impact the parameter values, for instance real time sensing enabled us to know the temperature and VOC variations that dramatically appeared once AHU and AC were active, which enabled better planning of placing equipment in the room in areas with minimum deviations of these values.

Lab self-maintenance. Real-time sensing of VOC content enabled us to build-in reactive intelligence within the system where the positive air pressure unit became responsive to the conditions of the room and varied activity based on the VOC levels with immediate responsiveness to minimise deviation from the safe values. Similarly the air-conditioning unit was guided by 24/7 temperature sensors to vary activity for stable temperatures in the room.

Lab Door Impact. Another unique setup tried in the lab was monitoring the door in relation to lab conditions especially, VOC content. During a fully functioning lab week automated trends were generated about the impact of door opening on room environment during peak operation. This resulted in understanding and better protocol preparation to limit door access by the personnel during routine procedures. A future area of work is to anticipate such disruptions by way of door sensors to auto adjust the air conditioning system for temperature maintenance.

This element of anticipation by the IOT based sensing system can address adverse quality control situations before they occur. Predictive algorithms

recognising these trends can enable us to act well in time to prevent any embryo-averse situation being built up in lab. The parameter variations can be factored in not just on a daily or weekly basis, but to also incorporate an element of alertness in the system to consider unique situations that might arise suddenly due to different lab locations such as a lab location close to a busy highway (air pollutants), or busy restaurant (cooking oil residues) to sporadic cultural event impacts (such as Diwali in India when pollution goes up several times). This detailed sensing approach imparts a unique smartness/readiness to the lab for bypassing such variations well in time.

Conclusion

IOT sensors working as standalone devices can support a network of accurate sensors to streamline quality control in fertility labs where maintaining temperature, humidity and VOC levels is crucial for the fertility lab’s success. This system can also serve as an accurate feedback loop to trace back quality control lapses and allow correction of anomalies for smoother operation and consistency of results in the lab.

Datasets rich with clinical information can be coupled to obtain pathophysiological correlation which can improve outcomes of clinical decision-making and care on a more individualised basis.⁵ Long-term data collected from these IOT devices can not only provide never before analysed clinically relevant patterns but can also serve as a fertile foundation for future integration into artificial intelligence and machine learning technologies for more robust lab setup.

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Corresponding author:

Maninder Sra

Santaan Fertility Clinic and Research Institute Bhubaneswar

India

Email: maninder.sra@santaan.in

Conflict of interest. The authors declare no conflicts of interest.

Acknowledgements. Mr Prashanta Rout designed and installed the IOT sensor network at the facility. He

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also fine-tuned the backend algorithm to customise the data output as needed.

References

1. How clean is the air in your IVF lab? (2015) Available at:

http://blog.vitrolife.com/togetheralltheway/how- clean-is-the-air-in-your-ivf-lab accessed on 22 December, 2016.

2. Hartshorne M. Challenges of the EU ‘tissues and cells’ directive. Reprod Biomed Online

2005;11(4):404-407.

3. Khoudja RY, Xu Y, Li T, Zhou C. Better IVF outcomes following improvements in laboratory air quality. J Assist Reprod Gen 2013;30(1):69- 76.

4. Graphs created through: https://plot.ly/create/

5. White RM, Paprotny I, Doering F, et al. Sensors and Apps for Community-Based Atmospheric Monitoring. EM: Air and Waste Management Association's Magazine for Environmental Managers. 2012; (MAY):36-40.