title: Artificial intelligence against breast cancer (A.N.N.E.S-B.C.-Project).
abstract: BACKGROUND
  Our preliminary study examined the development of an advanced innovative technology with the objectives of--developing methodologies and algorithms for a Artificial Neural Network (ANN) system, improving mammography and ultra-sonography images interpretation;--creating autonomous software as a diagnostic tool for the physicians, allowing the possibility for the advanced application of databases using Artificial Intelligence (Expert System).
  
  
  METHODS
  Since 2004 550 F patients over 40 yrs old were divided in two groups: 1) 310 pts underwent echo every 6 months and mammography every year by expert radiologists. 2) 240 pts had the same screening program and were also examined by our diagnosis software, developed with ANN-ES technology by the Engineering Aircraft Research Project team. The information was continually updated and returned to the Expert System, defining the principal rules of automatic diagnosis.
  
  
  RESULTS
  In the second group we selected: Expert radiologist decision; ANN-ES decision; Expert radiologists with ANN-ES decision. The second group had significantly better diagnosis for cancer and better specificity for breast lesions risk as well as the highest percentage account when the radiologist's decision was helped by the ANN software. The ANN-ES group was able to select, by anamnestic, diagnostic and genetic means, 8 patients for prophylactic surgery, finding 4 cancers in a very early stage.
  
  
  CONCLUSIONS
  Although it is only a preliminary study, this innovative diagnostic tool seems to provide better positive and negative predictive value in cancer diagnosis as well as in breast risk lesion identification.
authors.affiliation: Department of Gerontology, Geriatrics and Metabolic Disease, Second University of Naples, Naples, Italy. d_parmeggiani@yahoo.it
authors.forename: Domenico
authors.initials: D
authors.lastname: Parmeggiani
authors.forename: Nicola
authors.initials: N
authors.lastname: Avenia
authors.forename: Alessandro
authors.initials: A
authors.lastname: Sanguinetti
authors.forename: Roberto
authors.initials: R
authors.lastname: Ruggiero
authors.forename: Giovanni
authors.initials: G
authors.lastname: Docimo
authors.forename: Mattia
authors.initials: M
authors.lastname: Siciliano
authors.forename: Pasquale
authors.initials: P
authors.lastname: Ambrosino
authors.forename: Imma
authors.initials: I
authors.lastname: Madonna
authors.forename: Roberto
authors.initials: R
authors.lastname: Peltrini
authors.forename: Umberto
authors.initials: U
authors.lastname: Parmeggiani
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000453:epidemiology / Q000601:surgery; D002278:Carcinoma in Situ / Q000175:diagnosis* / Q000453:epidemiology / Q000601:surgery; D018270:Carcinoma, Ductal, Breast / Q000175:diagnosis* / Q000453:epidemiology / Q000601:surgery; D003937:Diagnosis, Differential; D055088:Early Detection of Cancer; D005103:Expert Systems; D005260:Female; D006801:Humans; D007558:Italy / Q000453:epidemiology; D008327:Mammography / Q000379:methods; D008875:Middle Aged; D010865:Pilot Projects; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D016896:Treatment Outcome