the manual determination of cellular differentials in blood , bone marrow , and body fluids ( including urine and pleural , peritoneal , and pericardial fluids ) is a well - established diagnostic method useful in the evaluation of hematopoiesis , hematopoietic disorders , and infectious diseases .
similar methods are also employed in establishing the grade of various solid tumors in tissue samples , a feature that often has a significant impact on prognosis . for decades
in fact , despite the development of automated cell counters based on flow cytometric methods , image recognition software , and other approaches , manual cell counting remains the gold standard for many of these applications . despite the importance of manual cell counting methods within the clinical laboratory ,
analog cell counters have remained essentially devoid of any significant improvement since their inception . as a result , these devices suffer from a number of substantial deficiencies .
not only are tabletop cell counters relatively expensive ( often more than five hundred dollars for a single device ) , but this expense is compounded by the need to purchase units in sufficient quantities to accommodate each microscope used for cellular differentials .
furthermore , only 8 and 12 button configurations are widely available , frequently resulting in either a dearth or an excess of buttons than is required for a particular task .
the buttons themselves are often closely spaced , and they can not be distinguished without visual confirmation , increasing the risk for operational error .
importantly , analog counters also commonly lack editing capabilities , making error correction highly cumbersome .
the inability to directly transfer these results to laboratory information systems increases the time needed to generate reports and introduces the possibility of typographical errors during data entry .
clearly , any erroneous results have the potential to jeopardize patient safety . with the recent advent of tablet computers , digital cell counters  in the form of mobile applications (  apps  )  have become an alternative to analog devices , having the potential to address many of their shortcomings .
relative to analog units , digital cell counters and their associated hardware are less expensive ( at the time this article was written , android - based tablets ranged in price from one to three hundred dollars , and cell counter apps were either free to download or , at most , five dollars per user ) and offer customizable user - interface configurations for optimal performance of a particular task .
as digital counters can be installed on any tablet computer or smartphone , they are significantly more portable than their tabletop counterparts .
importantly , data generated by digital counters can be edited during collection , and the final results are easily exported and/or digitally transferred without the need for manual entry of individual values . despite their reduced cost and broad availability , app - based cell counters have not been widely adopted by clinical laboratories . although many factors likely impede the acceptance of digital cell counters within the laboratory setting , the most substantive concern  particularly in terms of its potential impact on patient care  is a perceived decrease in count accuracy , potentially owing to the lack of tactile feedback inherent to a touchscreen interface . such concerns may have previously been understandable as validation data establishing the equivalency of digital cell counters to analog devices have not been reported . to address this deficiency
, we herein provide the first systematic evidence that digital / app - based cell counters function equivalently to standard tabletop devices .
our cell counter app was programmed using java by computer science students at san jose state university ( san jose , california : amir eibagi , minh dang , and jake karnes , under the supervision of dr .
following initial troubleshooting by our group , the app was distributed to faculty and staff at the university of california , san francisco ( ucsf ) medical center for additional testing .
further improvements ( incorporating end - user feedback from ucsf faculty and staff ) were implemented through a similarly iterative process .
after preliminary data had suggested that no systematic differences existed between digital and analog device - generated data , the app was made publicly available at : https://play.google.com/store/apps/details?id=edu.sjsu.hemepathcounter .
additional images are available at https://play.google.com/store/apps/details?id=edu.sjsu.hemepathcounter 188 peripheral blood smears and 62 bone marrow aspirate smears ( 250 total samples ) were selected , each for paired analysis using both the app - based counter and the tabletop unit . to ensure that a wide range of differential values for each cellular subset would be available for analysis , peripheral blood and bone marrow aspirate smears were selected from both the ucsf hematopathology teaching archives and from among de - identified patient samples .
all cases were selected randomly , and the reviewer was blinded to the final diagnosis as well as the reported differential count .
four authors ( at , bb , jd , and mj ) performed paired 100-cell differentials on a subset of the selected peripheral blood and/or bone marrow aspirate smears , once using our app - based cell counter ( cell counter for android , installed on one of three mobile devices : samsung galaxy s3 , samsung galaxy tab 4 7.0 , or samsung galaxy note 10.1 ; samsung electronics ) , and once with a with a standard tabletop unit ( modulus data systems diffcount ) . to streamline data acquisition and presentation ,
a total of six cell categories were quantified for each sample type : neutrophils ( including band forms ) , immature granulocytes , lymphocytes , monocytes , eosinophils , and basophils for peripheral blood smears ; mature granulocytes ( including band forms ) , immature granulocytes , lymphocytes , monocytes , erythroid precursors , and blasts for bone marrow aspirate smears .
differences between paired data sets were evaluated using the correlation coefficient , student 's t - test for paired samples , and bland 
altman plots , according to the well - established method described by bland and altman for establishing agreement .
primary data were aggregated across users [ figures 13 ] with individual user data analyzed separate [ tables 1 and 2 ] .
altman plots ( lower ) generated following paired counts of one hundred and 88 peripheral blood smears .
the relative proportions of neutrophils , ( a ) immature granulocytes , ( b ) lymphocytes , ( c ) monocytes , ( d ) eosinophils and ( e ) basophils , ( f ) were measured .
correlation coefficients ( r - values ) and results of paired t - tests ( p values ) are shown for each data set scatterplots with lines of linear regression ( upper ) and bland - altman plots ( lower ) generated following paired counts of 62 bone marrow aspirate smears .
the relative proportions of mature granulocytes ( a ) , immature granulocytes ( b ) , lymphocytes ( c ) , monocytes ( d ) , erythroid precursors ( e ) , and blasts ( f ) were measured .
correlation coefficients ( r - values ) and results of paired t - tests ( p values ) are shown for each data set individualized correlation coefficients ( r - values ) and results of paired t - tests ( p values ) for all six peripheral blood cell categories individualized correlation coefficients ( r - values ) and results of paired t - tests ( p values ) for all six bone marrow aspirate cell categories
our cell counter app was programmed using java by computer science students at san jose state university ( san jose , california : amir eibagi , minh dang , and jake karnes , under the supervision of dr .
following initial troubleshooting by our group , the app was distributed to faculty and staff at the university of california , san francisco ( ucsf ) medical center for additional testing .
further improvements ( incorporating end - user feedback from ucsf faculty and staff ) were implemented through a similarly iterative process .
after preliminary data had suggested that no systematic differences existed between digital and analog device - generated data , the app was made publicly available at : https://play.google.com/store/apps/details?id=edu.sjsu.hemepathcounter .
188 peripheral blood smears and 62 bone marrow aspirate smears ( 250 total samples ) were selected , each for paired analysis using both the app - based counter and the tabletop unit . to ensure that a wide range of differential values for each cellular subset would be available for analysis ,
peripheral blood and bone marrow aspirate smears were selected from both the ucsf hematopathology teaching archives and from among de - identified patient samples .
all cases were selected randomly , and the reviewer was blinded to the final diagnosis as well as the reported differential count .
four authors ( at , bb , jd , and mj ) performed paired 100-cell differentials on a subset of the selected peripheral blood and/or bone marrow aspirate smears , once using our app - based cell counter ( cell counter for android , installed on one of three mobile devices : samsung galaxy s3 , samsung galaxy tab 4 7.0 , or samsung galaxy note 10.1 ; samsung electronics ) , and once with a with a standard tabletop unit ( modulus data systems diffcount ) . to streamline data acquisition and presentation ,
a total of six cell categories were quantified for each sample type : neutrophils ( including band forms ) , immature granulocytes , lymphocytes , monocytes , eosinophils , and basophils for peripheral blood smears ; mature granulocytes ( including band forms ) , immature granulocytes , lymphocytes , monocytes , erythroid precursors , and blasts for bone marrow aspirate smears .
differences between paired data sets were evaluated using the correlation coefficient , student 's t - test for paired samples , and bland 
altman plots , according to the well - established method described by bland and altman for establishing agreement .
primary data were aggregated across users [ figures 13 ] with individual user data analyzed separate [ tables 1 and 2 ] .
altman plots ( lower ) generated following paired counts of one hundred and 88 peripheral blood smears .
the relative proportions of neutrophils , ( a ) immature granulocytes , ( b ) lymphocytes , ( c ) monocytes , ( d ) eosinophils and ( e ) basophils , ( f ) were measured .
correlation coefficients ( r - values ) and results of paired t - tests ( p values ) are shown for each data set scatterplots with lines of linear regression ( upper ) and bland - altman plots ( lower ) generated following paired counts of 62 bone marrow aspirate smears .
the relative proportions of mature granulocytes ( a ) , immature granulocytes ( b ) , lymphocytes ( c ) , monocytes ( d ) , erythroid precursors ( e ) , and blasts ( f ) were measured .
correlation coefficients ( r - values ) and results of paired t - tests ( p values ) are shown for each data set individualized correlation coefficients ( r - values ) and results of paired t - tests ( p values ) for all six peripheral blood cell categories individualized correlation coefficients ( r - values ) and results of paired t - tests ( p values ) for all six bone marrow aspirate cell categories
inspection of the differential results generated by the tabletop unit confirmed that the selected material encompassed a wide range of cellular differentials associated with a variety of neoplastic and nonneoplastic disorders .
these findings were considered to be representative of the range of differential results typically encountered in the clinical laboratories at ucsf medical center , an academic tertiary care hospital . as expected , a significant proportion of the patient samples taken from the hematopathology service were , upon unblinding , found to have no significant pathologic abnormalities ; these smears exhibited differential counts within established age - matched references ranges by both the standard and app - based methods .
having established that our selected cases spanned a wide range of values for each cellular subset under investigation , we turned our attention to the evaluation of similarity between the paired analog- and app - based counts of peripheral blood smears . with the exception of basophils ( r = 0.684 ) ,
peripheral blood differential cell counts were found to be highly correlated within each cell category ( mean r = 0.956 ; figure 2 , upper panels ) . despite their relatively low correlation coefficient , basophils showed a high level of agreement between values generated by the two methods , and the values from the analog- and app - based methods never differed by an absolute value > 3% .
altman plots uniformly exhibited a narrow spread about the mean difference without evidence of significant outliers [ figure 2 , lower panels ] .
paired t - tests demonstrated p values which did not reach statistical significance for any cell type ( all p > 0.05 , including basophils ) , arguing against the presence of any systematic deviation from the tabletop unit by the app - based counter . to assess for inter - user variability in count accuracy , we also calculated r and p values separately for each user [ table 1 ] . among 24 comparisons ,
only a single nominally statistically significant result was observed ( user mj , lymphocytes , p = 0.028 ) .
given the consistent similarity of differentials generated by the two methods in peripheral blood , we proceeded with the same analysis in bone marrow aspirate smears .
comparable results were obtained for bone marrow aspirate differential cell counts : the two data sets were highly correlated ( mean r = 0.990 ; figure 3 , upper panels ) ; showed a tight , random dispersion about the mean difference on bland 
altman plots [ figure 3 , lower panels ] ; and were not found to significantly differ by paired t - tests ( all p > 0.05 ) .
inspection of the differential results generated by the tabletop unit confirmed that the selected material encompassed a wide range of cellular differentials associated with a variety of neoplastic and nonneoplastic disorders .
these findings were considered to be representative of the range of differential results typically encountered in the clinical laboratories at ucsf medical center , an academic tertiary care hospital . as expected , a significant proportion of the patient samples taken from the hematopathology service were , upon unblinding , found to have no significant pathologic abnormalities ; these smears exhibited differential counts within established age - matched references ranges by both the standard and app - based methods .
having established that our selected cases spanned a wide range of values for each cellular subset under investigation , we turned our attention to the evaluation of similarity between the paired analog- and app - based counts of peripheral blood smears . with the exception of basophils ( r = 0.684 ) ,
peripheral blood differential cell counts were found to be highly correlated within each cell category ( mean r = 0.956 ; figure 2 , upper panels ) . despite their relatively low correlation coefficient , basophils showed a high level of agreement between values generated by the two methods , and the values from the analog- and app - based methods never differed by an absolute value > 3% .
altman plots uniformly exhibited a narrow spread about the mean difference without evidence of significant outliers [ figure 2 , lower panels ] .
paired t - tests demonstrated p values which did not reach statistical significance for any cell type ( all p > 0.05 , including basophils ) , arguing against the presence of any systematic deviation from the tabletop unit by the app - based counter . to assess for inter - user variability in count accuracy , we also calculated r and p values separately for each user [ table 1 ] . among 24 comparisons ,
only a single nominally statistically significant result was observed ( user mj , lymphocytes , p = 0.028 ) .
given the consistent similarity of differentials generated by the two methods in peripheral blood , we proceeded with the same analysis in bone marrow aspirate smears .
comparable results were obtained for bone marrow aspirate differential cell counts : the two data sets were highly correlated ( mean r = 0.990 ; figure 3 , upper panels ) ; showed a tight , random dispersion about the mean difference on bland 
altman plots [ figure 3 , lower panels ] ; and were not found to significantly differ by paired t - tests ( all p > 0.05 ) .
to our knowledge , a formal evaluation of the concordance between digital / app - based cell counters and analog tabletop units  considered to be the gold standard instrument for use in the determination of manual cellular differentials  has never been performed .
our work addresses this deficiency by providing the first evidence that cellular differentials generated by an app - based cell counter do not significantly differ from those of an analog device .
this conclusion is supported by the tight correlation ( r  0.899 for all measured cell categories , excluding peripheral blood basophils ) and lack of statistically significant differences observed between paired data sets ( aggregated p > 0.05 for all measured cell categories ) as well as by bland 
altman analysis , which demonstrated a narrow spread of the difference about the mean without evidence of systematic bias between paired results .
analysis of individual user - device pairs showed similar results , save for a single nominally significant difference observed for one user ( mj ) within one cell category ( peripheral blood lymphocytes ) .
however , as this value was not corrected for multiple comparisons ( according to the method of bonferroni ) , we do not consider it to be a contradictory datum .
although statistical measures of difference ( such as the chi - squared test , student 's t - test , etc . )
are familiar to pathologists and laboratorians alike , the approach to demonstrating similarity between data sets is often less well - defined .
some studies rely exclusively on the correlation coefficient to establish statistical similarity ; however , the combination of the correlation coefficient with bland 
altman analysis is a more stringent approach to demonstrating functional equivalence between a novel and an established method and is subject to fewer sources of error .
the superiority of a combined approach is evidenced by our results for peripheral blood basophils : although only moderate correlation was observed for this cell category ( r = 0.684 ; figure 2f , upper panel ) , the accompanying bland  altman analysis [ figure 2f , lower panel ] demonstrated that this lack of correlation does not represent a systematic discrepancy between the two methods . the nonsignificant difference demonstrated by
we therefore hypothesize that this finding is attributable to the enhanced effect of background variance on this low - frequency event . taken together
, the findings suggest that our app - based cell counter is functionally similar to an analog tabletop unit for the purpose of generating manual cellular differentials  a conclusion which is based on data generated by four different users on three separate touchscreen devices .
accordingly , our findings do not support the hypothesis that the loss of tactile feedback inherent to a digital / app - based cell counter leads to a decrease in count accuracy .
our app provides alternative modes of user feedback through differential coloring and distinctive sound effects for each button , which may compensate for the lack of button tactility ( future versions of our app may also include haptic / vibrational feedback with each keystroke ) . additionally , the screen sizes of the devices used in our study ranged from 4.8 inches ( samsung galaxy s3 ) to 10.1 inches ( samsung galaxy note 10.1 ) , suggesting that touchscreen dimensions do not significantly impact count accuracy
nevertheless , future studies to formally examine the effect of screen size , resolution , and/or orientation ( horizontal versus vertical ) are warranted to confirm this impression .
although our results can not be directly extrapolated to all cell counting apps and/or touchscreen devices , our study provides a framework for the validation of other software / hardware combinations . unlike digital counters , tabletop devices are ubiquitous , durable , require little technical support , and are unlikely to be stolen .
thus , it seems doubtful that app - based counters will entirely supplant analog devices , at least in the immediate future .
however , given the low total cost of app - based counters ( 100 dollars at minimum , including hardware ) and their ease of validation ( as laid out in this work ) , digital cell counters may represent an appealing alternative for small - sized and/or newly established clinical laboratories .
although not formally investigated in this study , subjectively , none of the users noted an increase in the time required to perform cellular differentials when using our app - based counter .
nonetheless , the potential impact of digital cell counters on laboratory workflow is a topic in need of further study . in summary
, we have presented here the first statistical evidence that digital / app - based cell counters are functionally similar to analog tabletop devices in the generation of manual cellular differentials .
 introduction : for decades cellular differentials have been generated exclusively on analog tabletop cell counters . with the advent of tablet computers , digital cell counters  in the form of mobile applications (  apps  )  now represent an alternative to analog devices . 
 however , app - based counters have not been widely adopted by clinical laboratories , perhaps owing to a presumed decrease in count accuracy related to the lack of tactile feedback inherent in a touchscreen interface . 
 we herein provide the first systematic evidence that digital cell counters function similarly to standard tabletop units.methods:we developed an app - based cell counter optimized for use in the clinical laboratory setting . 
 paired counts of 188 peripheral blood smears and 62 bone marrow aspirate smears were performed using our app - based counter and a standard analog device . 
 differences between paired data sets were analyzed using the correlation coefficient , student 's t - test for paired samples and bland  
 altman plots.results:all counts showed excellent agreement across all users and touch screen devices . with the exception of peripheral blood basophils ( r = 0.684 ) , differentials generated for the measured cell categories within the paired data sets were highly correlated ( all r  0.899 ) . 
 results of paired t - tests did not reach statistical significance for any cell type ( all p > 0.05 ) , and bland  
 altman plots showed a narrow spread of the difference about the mean without evidence of significant outliers.conclusions:our analysis suggests that no systematic differences exist between cellular differentials obtained via app - based or tabletop counters and that agreement between these two methods is excellent . 
INTRODUCTION
METHODS
App Design and Development
Case Selection
Cell Counting
Statistical Analysis
RESULTS
Cases Selected for Analysis Span a Wide Range of Cellular Differentials
Manual Differentials Generated on Peripheral Blood Smears Using Tabletop Versus App-Based Cell Counters Show No Statistically Significant Differences
Manual Differentials Generated on Bone Marrow Aspirate Smears Using Tabletop Versus App-Based Cell Counters Show No Statistically Significant Differences
CONCLUSIONS