BACKGROUND: Early diagnosis of a number of endocrine diseases is theoretically possible by the examination of facial photographs. One of these is acromegaly. If acromegaly were found, early in the course of the disease, morbidity would be lessened and cures more likely. OBJECTIVES, DESIGN, PATIENTS, MEASUREMENTS: Our objective was to develop a computer program which would separate 24 facial photographs, of patients with acromegaly, from those of 25 normal subjects. The key to doing this was to use a previously developed database that consisted of three-dimensional representations of 200 normal person's heads (SIGGRAPH '99 Conference Proceedings, 1999). We transformed our 49, two-dimensional photos into three-dimensional constructs and then, using the computer program, attempted to separate them into those with and without the features of acromegaly. We compared the accuracy of the computer to that of 10 generalist physicians. A second objective was to examine, by a subjective analysis, the features of acromegaly in the normal subjects of our photographic database. RESULTS: The accuracy of the computer model was 86%; the average of the 10 physicians was 26%. The worst individual physician, 16%, the best, 90%. The faces of 200 normal subjects, the original faces in the database, could be divided into four groups, averaged by computer, from those with fewer to those with more features of acromegaly. CONCLUSIONS: The present computer model can sort photographs of patients with acromegaly from photographs of normal subjects and is much more accurate than the sorting by practicing generalists. Even normal subjects have some of the features of acromegaly. Screening with this approach can be improved with automation of the procedure, software development and the identification of target populations in which the prevalence of acromegaly may be increased over that in the general population.
BACKGROUND: Early diagnosis of a number of endocrine diseases is theoretically possible by the examination of facial photographs. One of these is acromegaly. If acromegaly were found, early in the course of the disease, morbidity would be lessened and cures more likely. OBJECTIVES, DESIGN, PATIENTS, MEASUREMENTS: Our objective was to develop a computer program which would separate 24 facial photographs, of patients with acromegaly, from those of 25 normal subjects. The key to doing this was to use a previously developed database that consisted of three-dimensional representations of 200 normal person's heads (SIGGRAPH '99 Conference Proceedings, 1999). We transformed our 49, two-dimensional photos into three-dimensional constructs and then, using the computer program, attempted to separate them into those with and without the features of acromegaly. We compared the accuracy of the computer to that of 10 generalist physicians. A second objective was to examine, by a subjective analysis, the features of acromegaly in the normal subjects of our photographic database. RESULTS: The accuracy of the computer model was 86%; the average of the 10 physicians was 26%. The worst individual physician, 16%, the best, 90%. The faces of 200 normal subjects, the original faces in the database, could be divided into four groups, averaged by computer, from those with fewer to those with more features of acromegaly. CONCLUSIONS: The present computer model can sort photographs of patients with acromegaly from photographs of normal subjects and is much more accurate than the sorting by practicing generalists. Even normal subjects have some of the features of acromegaly. Screening with this approach can be improved with automation of the procedure, software development and the identification of target populations in which the prevalence of acromegaly may be increased over that in the general population.
Authors: M A E M Wagenmakers; S H P P Roerink; T J J Maal; R H Pelleboer; J W A Smit; A R M M Hermus; S J Bergé; R T Netea-Maier; T Xi Journal: Pituitary Date: 2015-02 Impact factor: 4.107
Authors: Luis V Syro; Fabio Rotondo; Alex Ramirez; Antonio Di Ieva; Murat Aydin Sav; Lina M Restrepo; Carlos A Serna; Kalman Kovacs Journal: Front Endocrinol (Lausanne) Date: 2015-06-12 Impact factor: 5.555
Authors: Karina Danilowicz; Patricia Fainstein Day; Marcos P Manavela; Carlos Javier Herrera; María Laura Deheza; Gabriel Isaac; Ariel Juri; Debora Katz; Oscar D Bruno Journal: Pituitary Date: 2016-08 Impact factor: 4.107