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

• US 5,463,548 A
• Filed: 04/28/1993
• Issued: 10/31/1995
• Est. Priority Date: 08/28/1990
• Status: Expired due to Term

• Current Assignee: ARCH Development Corporation (University of Chicago)
• Original Assignee: ARCH Development Corporation (University of Chicago)
• Inventors: Asada, Naoki, Doi, Kunio
• Primary Examiner(s): HUNTLEY, DAVID
• Application Number:
  US08/053,345
• Time in Patent Office: 916 Days
• Field of Search: 364/413.01, 364/413.02, 364/413.13, 395/11, 395/20-23
• US Class Current: 600/300
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 70/60 relating to pathologies
• CPC Class Codes: Y10S 128/922 including image analysis
• CPC Class Codes: Y10S 128/924 using artificial intelligence
• CPC Class Codes: Y10S 128/925 Neural network

Abstract

A method and system for computer-aided differential diagnosis of diseases, and in particular, computer-aided differential diagnosis using neural networks. A first embodiment of the neural network distinguishes between a plurality of interstitial lung diseases on the basis of inputted clinical parameters and radiographic information. A second embodiment distinguishes between malignant and benign mammographic cases based upon similar inputted clinical and radiographic information. The neural networks were first trained using a hypothetical data base made up of hypothetical cases for each of the interstitial lung diseases and for malignant and benign cases. The performance of the neural network was evaluated using receiver operating characteristics (ROC) analysis. The decision performance of the neural network was compared to experienced radiologists and achieved a high performance comparable to that of the experienced radiologists. The neural network according to the invention can be made up of a single network or a plurality of successive or parallel networks. The neural network according to the invention can also be interfaced to a computer which provides computerized automated lung texture analysis to supply radiographic input data in an objective and automated manner.

Claims

1. A method for differential diagnosis of a plurality of predetermined interstitial lung diseases, 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 interstitial lung diseases characteristics;

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;

inputting said transformed numerical expressions into a plurality of successive neural networks; and

diagnosing at least one of said plurality of interstitial lung diseases using said neural networks in accordance with said input expressions including;

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.

2. 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.

3. 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.