Method and system for differential diagnosis based on clinical and radiological information using artificial neural networks

A method for differential diagnosis of a plurality of interstitial lung diseases using an incomplete set of input data, comprising:

• selecting a plurality of clinical parameters defining characteristics of a subject;
• selecting a plurality of radiographic descriptors comprised of predetermined features obtained from a radiographic chest image defining characteristics of interstitial lung diseases;
• forming a complete set of input data comprised by said plurality of clinical parameters and said plurality of radiographic descriptors;
• converting said plurality of clinical parameters and said plurality of radiographic descriptors into numerical expressions;
• transforming each of said numerical expressions into a number in a predetermined range;
• training a neural network comprising a plurality of successive neural networks to identify each of said plurality of interstitial lung diseases using a database of said complete set of data;
• inputting said transformed numerical expressions into said neural network, said input expressions representing incomplete sets of input data;
• and
• diagnosing at least one of said plurality of interstitial lung diseases using said neural network in accordance with said input expressions, including:
• distinguishing between normal and abnormal patterns in said radiological chest image using a first of said plurality of successive neural networks;
• distinguishing between said plurality of predetermined interstitial lung diseases and other diseases using a second of said plurality of successive neural networks;
• and
• distinguishing between said plurality of predetermined interstitial lung diseases using a third of said plurality of successive neural networks.

A method for differential diagnosis of a plurality of predetermined interstitial lung diseases using an incomplete set of input data, comprising:

• selecting a plurality of clinical parameters defining characteristics of a subject;
• selecting a plurality of radiographic descriptors comprised of predetermined features obtained from a radiographic chest image defining characteristics of interstitial lung diseases, including at least plural of the following fourteen radiographic descriptors:
• distribution of infiltrates in 6 lung zones,
• homogeneity, fineness, nodularity, septal lines and honeycombing of said infiltrates, and
• lymphadenopathy, pleural effusions and heart size;
• forming a complete set of input data comprised by said plurality of clinical parameters and said plurality of radiographic descriptors;
• converting said plurality of clinical parameters and said plurality of radiographic descriptors into numerical expressions;
• transforming each of said numerical expressions into a number in a predetermined range;
• training a neural network comprising a plurality of successive neural networks to identify each of said plurality of predetermined interstitial lung diseases using a database of said complete set of data;
• inputting said transformed numerical expressions into said neural network, said input expressions representing incomplete sets of input data;
• diagnosing at least one of said plurality of predetermined interstitial lung diseases using said neural network in accordance with said input expressions;
• distinguishing between normal and abnormal patterns in said radiographic chest image using a first of said plurality of successive neural networks;
• distinguishing between said plurality of predetermined interstitial lung diseases and other diseases using a second of said plurality of successive neural networks;
• and
• distinguishing between said plurality of predetermined interstitial lung diseases using a third of said plurality of successive neural networks.