Artificial Intelligence against Breast Cancer (A.N.N.E.S-B.C.-Project)


COD: 01_2012_001-6 Categorie: ,

Domenico Parmeggiani, Nicola Avenia, Alessandro Sanguinetti, Roberto Ruggiero, Giovanni Docimo, Mattia Siciliano, Pasquale Ambrosino, Imma Madonna, Roberto Peltrini, Umberto Parmeggiani

Ann. Ital. Chir., 2012 83: 1-6

La mia nuova descrizione qui!

Price of a print issue €25.00

INTRODUCTION: 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). MATERIALS AND 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 ANNES 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 ANNES 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 ANNES group was able to select, by anamnestic, diagnostic and genetic means, 8 patients for prophylactic surgery, finding 4 cancers in a very early stage. DISCUSSION AND CONCLUSION: 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.