The team is led by researchers at the Wallace H. Anemia is a condition caused by a low red-blood-cell count or a low level of the protein hemoglobin in red blood cells. The iron in hemoglobin carries oxygen to fuel the many processes of metabolism that occur throughout our bodies. Anemia takes many forms, from a mild condition that can be improved by dietary changes, to chronic anemia, that requires vigilant monitoring and possibly transfusions of blood for a lifetime.
People who are chronically anemic require frequent hemoglobin-level testing to monitor their disease and to guide their clinical treatment. The gold-standard test to measure hemoglobin is a complete blood count, based on a blood draw performed by trained technicians and processed in a laboratory using expensive equipment and reagents. Despite the high prevalence of anemia, there has been no noninvasive, inexpensive, and accurate hemoglobin assessment technology available that enables chronic anemia patients to better self-manage their disease.
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Importantly, inter-physician variability of Hgb level estimates were high, as indicated by the low level of agreement with an ICC of 0. App outperforms hematologists in physical exam-based hemaglobin measurement.
Plots a , c represent the pooled results of 5 board-certified hematologists estimating blood hemoglobin levels based on images of patients fingernails. Given the performance of this technology and high prevalence of anemia worldwide, afflicting nearly two billion people, especially young children, the elderly, and pregnant women, worldwide, this completely noninvasive technology that requires only photos obtained from smartphones has significant implications as a widely accessible screening tool for at risk populations and the general population.
The ability to inexpensively diagnose anemia with a high sensitivity, completely noninvasively and without the need for any external smartphone attachments or calibration equipment represents a significant improvement over current POC anemia screening. The external equipment requirements of current existing POC anemia screening technologies represent a significant hurdle for use, as each additional piece of equipment requires a supply chain to support it.
For example, even relatively low-cost color calibration cards used to normalize for different background lighting require distribution to the patient and quality control measures regarding the manufacturing process to ensure that the colors are printed precisely and accurately on each card. In addition, while our system can be used for both anemia screening and diagnosis, it is important to contextualize the accuracy requirements of these different clinical scenarios.
Our results indicate that this smartphone app is ideally suited for screening anemia. Furthermore, the results of the app when individually calibrated suggest that this technology may, with further study, achieve Hgb measurement accuracy necessary for anemia diagnosis. Going forward, we will continue to increase enrollment in our individual calibration studies to confirm the high level of diagnostic accuracy that would be necessary to truly replace blood-based anemia testing.
We present specific use cases highlighting the difference between anemia screening vs monitoring and diagnosis in Supplementary Figure 7. Optimizing sensitivity is of paramount importance for a screening tool, due to the ability to correctly identify a high percentage of anemia cases even if this negatively impacts specificity.
In its current form, our technology requires the user to simply obtain a fingernail image, which can then be analyzed with an on-board smartphone app that comprises our image analysis algorithm to output the Hgb level measurement or be transmitted remotely to another device e.
After identifying subjects that may possibly be anemic, either type of system can recommend confirmatory Hgb level testing with a CBC, allowing any false positives to avoid unnecessary treatment.
Given the ever-increasing rate and near ubiquity of smartphone ownership worldwide 36 , this noninvasive, inexpensive, patient-operated Hgb measurement algorithm allows those at risk of anemia to monitor their conditions using only the native hardware included with their own smartphone 2 , 3.
This is particularly pertinent in low resource settings, where, contradictory to the relative lack of medical infrastructure, mobile phone networks are quite extensive and have leapfrogged landlines Additionally, this system has the potential to fundamentally alter the management of patients with chronic anemia. During the course of several weeks, a patient may take images of their fingernail beds and enter their CBC-measured Hgb levels that were obtained as part of their regular outpatient clinical care.
Results suggest that these images and Hgb levels may be used to teach the smartphone phone to develop a calibration personalized and tailored to each individual patient. In times of clinical stress, these patients, such as those with genetic causes of anemia or cancer undergoing chemotherapy, may experience rapid, life-threatening, precipitous drops in Hgb and require constant monitoring to determine their need for transfusions.
Using this technology, patients could potentially self-monitor their anemia from the comfort of their own home, rather than through inconvenient and recurring clinic visits. In addition, some patients with chronic anemia due to a genetic etiology require chronic transfusions to survive. These scheduled transfusions are currently administered at convenient and regular intervals, and not based on clinical need Hence, a patient may be transfused too early, exposing them to unnecessary transfusion-related effects i.
By enabling continuous and simple monitoring, this technique may empower patients and lead to better allocation of blood bank resources. Moreover, further data collection will increase the size of the patient image pool, facilitating the incorporation of deep machine learning Big Data techniques to further refine the Hgb measurement algorithm Furthermore, this CBC-validated, smartphone image-based smartphone app for measuring Hgb has the potential to dramatically improve upon the accuracy, cost, and convenience of current Hgb measurement devices while also eliminating the need for anything other than a smartphone, representing a significant improvement over other POC Hgb measurement technologies 7 , 8 , 20 , 21 , With this smartphone image-based Hgb measurement system, any person—healthy or ill—in any location, at any time, now has access to an important health metric and may seek care accordingly.
Moreover, healthcare officials in low resource settings may use this technology to inform allocation of limited healthcare resources e. 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.
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