Biomedical Engineering Reference
In-Depth Information
9.3 Diagnostic Sensitivity and Specificity
Thegoalofadiagnosticmethodistodetectallpeoplewithinapop-
ulation bearing the disease marker (no false-negative people) and
to have no false-positive results, i.e., positive signal from a healthy
person. The terms that characterize these demands are
=
/
Sensitivity
A
(A + C)
and
=
/
Specificity
D
(B + D)
with
“ill”
=
A+C
and
“healthy”
=
B+D
Further measures are:
Positive predictive value:
=
/
PPV
A
(A + B)
=
PPV
Sensitivity
·
Prevalence
=
Sensibility
·
Prevalence + (1 − Specificity)
·
(1 − Prevalence)
and
D
C+D
=
Negative predictive value:
NPV
=
NPV
Specificity
·
(1 − Prevalence)
=
·
·
Prevalence
A: right positive; B: false positive; C: false negative; D: right negative.
Diagnostic specificity and sensitivity are analyzed by receiver-
operator curves (ROC) using data obtained from defined healthy
populations, patients with diseases other than the investigated
ones, and patients with clinical relevance to the respective disease.
Specificity
(1 − Prevalence) + (1 − Sensitivity)
9.4 Software for the Lab
The following software acts for a lot of offers. The programs listed
below are, of course, excellent, but it should be checked if other
software is more convenient with respect to distinct advantages or
parameters.
Since the development of software is extremely quick, no ver-
sions are indicated. Most of the software is available for PC as well
as for Macintosh computers.
