Bilicam just completed a nationwide clinical trial of newborns. By analyzing how colors are absorbed and reflected across those wavelengths, it can detect concentrations of hemoglobin and other blood components like plasma. Next research steps include wider national and international testing of HemaApp, collecting more data to improve accuracy rates, and using smartphones to try to detect abnormal hemoglobin properties that could help screen for sickle cell disease and other blood disorders.
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Price Free. App Support Privacy Policy. Family Sharing With Family Sharing set up, up to six family members can use this app. This completely noninvasive, algorithm-based approach represents a paradigm shift in the way anemia can be screened, diagnosed, and monitored globally.
As the system requires no reagents or equipment, the healthcare cost savings could also be significant. Overall, the ability to conduct self-testing using an unmodified smartphone presents significant advantages over previously reported technologies which require additional equipment such as calibration cards and light-blocking rigs.
Moreover, the app utilizes metadata that is automatically obtained from the smartphone camera which enables normalization of background lighting conditions. This presents significant conceptual advantages over existing Hgb measurement technologies, as Hgb levels can now be measured by a patient without requiring a clinic visit or any cumbersome external equipment.
This system suffers from the potential to be impacted by diseases which cause nailbed discolorations such as jaundice and cyanosis 42 , However, it is important to point out that a large population of our study subjects suffered from hemolytic anemias, which can lead to jaundice. We found no correlation between disease state and Hgb measurement error, indicating that jaundice is unlikely to impact Hgb measurement Fig. Furthermore, the image analysis algorithm can potentially be trained in future studies on populations with these disorders to take these discolorations into account.
We would also argue that suffering from cardiovascular dysfunction sufficient to cause cyanosis, is a significant enough health problem to render anemia diagnosis a secondary concern, thus obviating the need for these patients to use this app under those circumstances.
While these conditions may present challenges in Hgb measurement, they present a promising opportunity to use the app to screen for such diseases. The primary limitations in this study were derived from the use of a single smartphone model and test administrator.
This study will also evaluate and improve upon our quality control measures. Overall, the ability to conduct rapid on-demand self-testing demonstrates the versatility of the system and could be especially conducive for global heath applications, where remote diagnosis coupled with tight quality control measures may be preferred and enabled by increasing smartphone use and mobile network prevalence in low resource settings This approach will shift the anemia screening paradigm worldwide by empowering patients to test themselves from the comfort of their own homes, wherever and whenever they desire.
Subjects were excluded by quality control measures if their images showed fingernail beds that were obscured or discolored due to leukonychia, nailbed injury, nail polish, darkening due to medication 44 , etc. Exclusions were conducted to eliminate unnecessary variables that could obfuscate algorithm development.
Smartphone pictures were obtained with the camera flash both on and off. Prior to imaging, the auto-focus and brightness adjustment of the smartphone camera was activated by tapping the screen in order to focus on the nailbed. If possible, subjects were encouraged to curl their fingers inwards with their palms facing upwards to control for possible alterations in blood flow caused by hand and finger positioning that could potentially affect the underlying color of the fingernail beds Fig.
Images were taken in clinic examination rooms, where lighting conditions and room illuminants were relatively consistent. An additional 72 healthy subjects from Emory University and The Georgia Institute of Technology were tested using an identical protocol.
All imaging was conducted in a room with similar lighting conditions to the clinic exam rooms, which was confirmed via digital light meter.
Fingernail bed images and blood Hgb levels were analyzed in a total of subjects. In six cases, fingernail polish was discovered after informed consent had been obtained, and these subjects were excluded from testing after study enrollment. Smartphone images were transferred or transmitted from the smartphone used in the study to a computer. Regions of interest, from which fingernail and skin color data were extracted, were manually selected to ensure that fingernail irregularities were excluded from analysis.
Color data were extracted from each region and averaged together across fingers for each subject. This was shown to be an acceptable method due to the low color variability between different fingers Supplementary Figure 9.
A uniform bias adjustment factor was also added to address the inherent variability in fingernail measurement. Two distinct use models and algorithms were applied for this Hgb measurement method: 1 as a noninvasive, smartphone-based, quantitative Hgb level diagnostic requiring calibration with CBC Hgb levels that enables chronic anemia patients to self-monitor their Hgb levels, and 2 as a noninvasive, smartphone-based anemia screening test that does not require calibration with CBC Hgb levels.
Sampling strategies were used to generate the algorithm depending on the specific application. Anemia screening among the general population: To develop the algorithm as a tool to screen for anemia, the entire study population subjects was randomly split into a discovery group subjects and a testing group subjects. The discovery group was used to establish the relationship between image parameters and Hgb levels via robust multi-linear regression, much like the calibration phase of the personalized calibration study.
A testing group, analogous to the testing phase of the personalized calibration study, of subjects was used to validate the resultant algorithm. Validation was performed by applying the smartphone algorithm to each testing image and comparing the algorithm generated Hgb result with the CBC Hgb result i.
Resulting data from most accurate outcome of this optimized screening algorithm is depicted in Fig. Hgb measurements taken from the previously described personalized calibration study were not included in this anemia screening study. Hgb levels in the chronic anemia patients fall throughout a 4-week transfusion cycle, which was chosen as an appropriate time interval for this study. Smartphone Images were obtained with and without the camera flash. Prior to each imaging session, CBC Hgb levels were obtained from each subject via venipuncture.
Color data and phone metadata were compiled and a relationship between image data and CBC Hgb levels was established via robust multi-linear regression. This process was repeated for each individual using data from the 4 weeks of images to create a unique calibration curve personalized for that individual.
Image parameter changes associated with Hgb level fluctuations specific to each person were related to perform algorithm calibration specific to each subject, thus improving the accuracy of Hgb level estimation. After the smartphone image analysis system was calibrated for each subject, Hgb levels were measured weekly over the next 4 weeks using the newly personalized algorithm. These Hgb level measurements were then compared to the CBC Hgb levels obtained at the same time to assess accuracy.
This personalized calibration occurred over a total of 8 weeks. Images were taken of 50 subjects fingernails from the previously described clinical study. Hematologists M. For comparison, images were loaded into the app, and the Hgb measurement protocol was performed on these images. All images and analysis were taken using an iPhone 5S.
It is important to note that these images were not used in the development of the underlying image analysis algorithm. Intraclass correlation coefficient ICC reflects not only degree of correlation but also agreement between measurements and ranges between 0 and 1, with values closer to 1 representing stronger reliability. Reliability refers to the degree of agreement among raters.
It gives a score of how much homogeneity, or consensus, there is in the ratings given by different judges or instruments. The ICC is able to incorporate the reliability of more than 2 raters-as in the case of the 5 hematologists evaluating nail beds. Patients and the physicians were assumed to be random samples from the respective populations they represent.
The Hgb level measurement algorithm was incorporated into mobile apps. All experiments complied with all relevant ethical guidelines for human subject research, namely, the Declaration of Helsinki. Verbal assent and written consent were obtained from all study subjects and their parents age permitting in accordance with HIPAA regulations prior to partaking in the study.
All experiments involving human subjects in this manuscript were approved by either the Emory University IRB algorithm development—approval number or the Georgia Institute of Technology IRB skin temperature and heart rate interference—approval number H A two-way random effects model was used to estimate our ICC for measuring agreement between the app and hematologists at measuring Hgb levels based on physical examination.
Any commercial use including the distribution, sale, lease, license, or other transfer of the code to a third party, is prohibited. The de-identified datasets collected and analyzed in this study i. Dorsey, E. State of telehealth. New Engl. Article Google Scholar. Wolf, J. Diagnostic inaccuracy of smartphone applications for melanoma detection.
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