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Computer aided diagnosis application in digital mammography and acute abdominal pain. By. Dr . Usama Gameel Bassuoni Executive manger of HMIS. Director of medical training Ahmed Maher Teaching Hospital. Medical decision process 3 element’s . · Information gathering · Data analysis
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application in digital mammography
and acute abdominal pain
Dr . Usama Gameel Bassuoni
Executive manger of HMIS
Director of medical training
Ahmed Maher Teaching Hospital
·“Human life is too important to be left to
Patients receive the best treatment on the
best evidence available
· Computers allow us to access up to date
· research is there to assist us not undermine our
Shared information advances our knowledge
. Encourages us to expand our practices
. Scrutiny of all research to validate validity
. No one person can have all expert knowledge
it is important to collaborate with all professional
·Use good karma always
·is an increasingly role as decision
·are being applied in several health care
areas e.g digital mammography and AAP
·Cancer Breast ♂ : ♀ 1-15
·40% of CA Breast Benign
· In Norway 2100 new cases,800 deaths/year
· Screening program based on mammography↓mortality
· ↑no of countries started mass screening programs→increasedno mammography interpretation.
densities of breast tissue
· 10 – 30 % of ca breast missed by radiologist
·CAD design to detect , classification clustered
micro calcification nodules
·Image segmentation : detection , extraction of clustered micro calcified nodules from back ground breast tissue
·Extracted micro calcification nodules categorized as benign or malignant ( image classification )
·Clustered micro calcification early signs ( potential cancerous changes)
· Micro calcification small ca ++ deposit accumulate is breast tissue as a bright spot
·Cluster defined to be at least 3 micro calcifications with in 1-2 cm region of mammogram
·Individual micro calcifications range 0.1 – 1.0 mm ( in mammogram)
·2nd opinion radiologist use result of pc analysis of mammogram in making a diagnosis
·Digital mammogram → feature extraction → classification → analysis → detected tumor.
feature extraction schemes :
·To generate segment the mammogram into 2classes clusters of micro calcification
·To generate features used to discrimination benign and malignant clusters of micro calcifications
Over last 20 Years studies in U.K , Europe in AAP
· 2-3.000.000 / year with AAP
·Cost Euro 1.5 million/ year
·AAP not a diagnosis in the strict sense but rather
·The most common frequent decision which surgeon called upon is not merely related to precise diagnosis but rather to operate or observe?
· General rules of investigation :
Know what you are doing? don’t waste time?, know what you want ?
· Initial diagnosis accuracy 45 %
·Perforated app rate 30% (delayed diagnosis ) , -ve app rate 30%
·In Europe last year 200.000 app removed too late , 200.000 normal app removed
. Patient centric data ( direct related to patient )
.Aggregates data based on performance and utilization
( resources management data)
.Transforming – based data for planning , clinical
and management decision support
.Comparativedata for health services researches.
CAD useful system for improving diagnosis and better clinical practice
Changes is like dragon fight it you will loose .,ignore it., it will eat you., success only favors those who challenge it. Patients needs change constantly so therefore we must respond accordingly to new advancement in technology