Hemoglobin Validation Report
Version 1.0

MED-000007

Hemoglobin Validation
Introduction
Hemoglobin (Hb) molecules are a set of very closely related proteins. Each Hb protein contains four ironatoms and can therefore carry four molecules of oxygen (O2). Hb attaches to red blood cells and delivers
oxygen throughout the body with blood flow. The main function of Hb in mammals is to transportoxygen from the lungs to the organs and tissues. Moreover, it also specifically interacts with the 3 othergases, carbon dioxide (CO2), carbon monoxide (CO), and nitric oxide (NO), that have important biologicalroles. Hb also plays a role in helping red blood cells obtain their disc-like shape, which helps them moveeasily through blood vessels.1–3
Hb levels are usually assessed by a Hb blood test. A low level of Hb in the blood means there is also a
low level of oxygen. This can result in a condition called anemia. Patients with anemia typically present
with vague symptoms such as lethargy, weakness, and tiredness. Severe anemia may present withsyncope, shortness of breath, and reduced exercise tolerance. Hb is usually measured in grams per
deciliter (g/dL) of blood. Normal Hb levels are usually 13-18 g/dL in males and 12-15 g/dL in nonpregnant females.4,5
To obtain a blood sample for Hb evaluation, an invasive blood test measurement is needed. Replacing
this method with a non-invasive, cheap, and remote technique is highly advantageous for availability,
affordance, and usability.
PPG (photoplethysmography) is a non-invasive, simple, and low-cost tool that can reflect blood flow andblood volume changes in blood vessels. The PPG waveform comprises a pulsatile ('AC') physiological
waveform attributed to blood volume changes with each heartbeat and is superimposed on a slowly
varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic
nervous system activity, and thermoregulation. PPG technology has been used in a wide range ofcommercially available medical devices to measure blood pressure, oxygen saturation, cardiac output,
and to assess autonomic function.6 Hb can also be evaluated by devices using PPG technology. FDA
approved such a device in a range of 8-17 gr/dl with a measurement error of ±1 gr/dl.7
Camera-based approaches make it possible to derive remote PPG (rPPG) signals, and therefore mightenable a remote and non-invasive measurement of blood parameters. Binah.ai’s Hb algorithm uses therPPG signal recorded from facial skin tissue. The algorithm extracts face video images, produces an rPPG
signal, analyzes the data using AI, and provides the end user with a Hb measurement in real-time.
This report describes the results of validation experiments that compare Binah.ai’s Hb evaluations withthe results of regular, invasive blood tests.

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Hemoglobin Validation Report
Version 1.0

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Methods
Binah.ai’s Hb measurements were compared to the Hb values received in regular blood tests of healthy
participants.
Measurement set-up:
Each participant was instructed to sit as stably as possible. Recordings were conducted in a testing room
located in Binah.ai’s offices, with controlled and fixed artificial ambient light. A mobile device was placedon a stand in front of the participant. The participant’s face filled over 20% of the frame’s area (distanceof about 30cm) and was positioned in the center of the frame. The camera was set at the level of theforehead and positioned perpendicular to the face. Participants were instructed to look at the screen
during the whole recording and to avoid any movement (including talking). Each recording lasted
approximately 60 seconds.
Statistical analysis:
Accuracy was calculated using the following parameters:

𝐴𝐸 (𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝐸𝑟𝑟𝑜𝑟) = 𝐴𝑝𝑝𝑖 ― 𝑅𝑒𝑓𝑖
∑𝑁(
― 𝑅𝑒𝑓𝑖)2
𝑖=1 𝐴𝑝𝑝𝑖

𝑅𝑀𝑆𝐸 =

𝑁

𝑀𝐴𝐸 =

1
𝑛

𝑛

|𝐴𝑝𝑝𝑖 ― 𝑅𝑒𝑓𝑖|
𝑖=1

When,
N is the number of data points.
App is the measurement of the Binah.ai’s application.
Ref is the reference results.
i is the index number of the measurements.
Confidence intervals (CI) were calculated using the bootstrap method and indicate where the estimator(i.e., RMSE) would fall in 95%, for future samples.
Participants with outlier AE (defined as 3 standard deviations or more above the mean) and participants
with invalid reference device values were excluded from analysis.
For this report, the Binah.ai’s SDK 4.10.1 version was used.
The measurements were recorded by the mobile device models listed below.
iOS: iPhone 11 Pro, iPhone 13 Pro, iPhone 13 Pro Max.
Android: Samsung S21 Ultra, Pixel 6 Pro.

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Results
Demographic Data:
Table 1 includes participants’ demographic data for each operating system (iOS and Android).
OperatingNumber of
Age Range
SystemParticipants(average)Sex
Fitzpatrick Skin Tone *iOS43
20-61 (30) F (59%), M (41%)
2 (41%), 3 (53%), 4 (6%)
Android42
20-61 (30) F (57%), M (43%)
2 (37%), 3 (56%), 4 (7%)
Table 1: Demographic data for experiments using phones with an iOS and Android operating systems.
* Fitzpatrick skin tone classifications are: 1- Pale white, 2- white, 3- Darker white, 4- Light brown, 5- Brown,
6- Dark brown or black.
Accuracy data:
Table 2 includes accuracy data for iOS and Android (RMSE, RMSE CI 95%, MAE±SD).
Operating
Blood
Number of
SystemParameter
measurements
RMSE
RMSE CI 95%
MAE±SDiOS
Hb (g/dl)
98
1.1[0.9, 1.2]
0.9±0.6
AndroidHb (g/dl)
81
1[0.9, 1.2]
0.8±0.6
Table 2: RMSE, RMSE CI 95%, and MAE±SD for experiments using phones with iOS and Android
operating systems, when compared to the reference results. CI were calculated using the bootstrap
method. Abbreviations: RMSE - Root Mean Square Error, CI - Confidence Intervals, MAE -Mean Absolute
Error, SD - Standard Deviation.
Pearson correlations between Binah.ai’s Hb estimations versus the results of regular invasive blood tests
were calculated and presented in Figure 1. Pearson correlation coefficients (R values) are presented on
the plots.
The Bland-Altman plots for comparison between measurements of the two methods (Binah’s and thereference results) are presented in Figure 2.

Figure 1: Binah.ai’s Hb estimation vs. reference results. Pearson correlation was calculated, andcorrelation coefficients are presented on each plot (R). Plots describe measurements conducted withboth operating systems (iOS and Android).

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Version 1.0

MED-000007

Figure 2: Bland-Altman plots for comparison between Hb measurements of the two methods (Binah’sand the reference results). Plots describe measurements conducted with both operating systems (iOSand Android). The “Bias” line stands for the mean difference between measurements of Binah.ai and thereference results, the “Error” lines represent the value of the accuracy criterion, the “Limits ofagreement” lines mark the limit of 95% of the samples.

Conclusions
This report summarizes the results of validation experiments in which Binah.ai’s Hb estimations werecorrelated with blood test results and had MAE, which is similar to an FDA-approved device7.

References
1.
2.
3.
4.
5.
6.
7.

Confidential

Schechter, A. N. Hemoglobin research and the origins of molecular medicine.
Blood, J. Am. Soc. Hematol. 112, 3927–3938 (2008).
Farid, Y., Bowman, N. S. & Lecat, P. Biochemistry, hemoglobin synthesis. (2019).
Pandya, N. K. & Sharma, S. Capnography and pulse oximetry. in StatPearls[Internet] (StatPearls Publishing, 2021).
Turner, J., Parsi, M. & Badireddy, M. Anemia. in StatPearls [Internet] (StatPearls
Publishing, 2022).
Guralnik, J. M., Ershler, W. B., Schrier, S. L. & Picozzi, V. J. Anemia in the elderly: apublic health crisis in hematology. ASH Educ. Progr. B. 2005, 528–532 (2005).
Allen, J. Photoplethysmography and its application in clinical physiologicalmeasurement. Physiol. Meas. 28, (2007).
Masimo. Masimo brochure - Noninvasive and Continuous Hemoglobin (SpHb®)
Monitoring. https://www.masimo.com/siteassets/us/documents/pdf/plm10283b_brochure_total_hemoglobin_sphb_us.pdf.

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