The differential diagnosis of vertical strabismus from prism cover test data using an artificially intelligent expert system

2007 ◽  
Vol 45 (7) ◽  
pp. 689-693 ◽  
Author(s):  
Anthony C. Fisher ◽  
Arvind Chandna ◽  
Ian P. Cunningham
Keyword(s):  
Differential Diagnosis ◽  
Expert System ◽  
Test Data ◽  
Cover Test

scholarly journals Web-StrabNet: A Web-Based Expert System for the Differential Diagnosis of Vertical Strabismus (Squint)

2010 ◽  
Vol 11 (1) ◽  
pp. 89-97 ◽  
Author(s):  
A. C. Fisher ◽  
S. P. Lake ◽  
I. P. Cunningham ◽  
A. Chandna
Keyword(s):  
Differential Diagnosis ◽  
Expert System ◽  
Clinical Data ◽  
Data Sets ◽  
Artificial Data ◽  
Web Based ◽  
Input Space ◽  
Mechanical Factors ◽  
Output Space ◽  
Cover Test

A squint, also known as strabismus, is a condition where the eyes are misaligned because of incorrect balance in the controlling eye muscles. This may result from muscular, neuromuscular or purely mechanical factors. An affected eye will have either predominating vertical or horizontal deviation. Vertical deviations are usually classified into eight classes (diagnoses) and horizontal into 10. The present work considers only the former but extension to the latter is straightforward.The differential diagnosis of strabismus is usually achieved in the prism cover test (PCT). A range of test prisms is presented to the eye and the resulting deviation in a particular direction of gaze is observed. In the full PCT, 10 positions of gaze are considered: in each position there are, say, 40 prisms of plus and minus power to investigate. The problem can be expressed as the inference 1-of-8 diagnoses in the output space from an input space of 10 parameters each with a resolution of 1-in-80. However, in the majority of clinical examinations, the corner-most positions of gaze are difficult to assess, particularly in children. Therefore, frequently the 6-position subset is reported requiring a corresponding reduction in the input space dimensions of 10 to 6. Web-StrabNet©is an expert system for the differential diagnosis of strabismus based on parallel instances of multi-layer perceptrons trained on exemplar data generated in consensus by two clinical experts. This machine expert is programmed in MatLab™ and is freely available as an Internet website (www.strabnet.com) which uses MatSOAP©, an XML/SOAP accessible automation server running a number of simultaneous MatLab instances.StrabNet achieves diagnostic accuracies of 100 and >94% with artificial data and typically ∼99 and ∼99% in clinical data-sets for the 10-position and 6-position subset PCT's, respectively.

Kraepelin: an expert system for the diagnosis of Obsessive - Compulsive Disorders

1991 ◽  
Vol 6 (4) ◽  
pp. 171-175 ◽  
Author(s):  
M Roca-Bennasar ◽  
A Garcia-Mas ◽  
N Llaneras ◽  
J Blat ◽  
P Roca
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Expert System ◽  
Natural Language ◽  
Personal Computer ◽  
Clinical History ◽  
User Acceptance ◽  
Obsessive Compulsive ◽  
Obsessive Compulsive Disorders ◽  
Compulsive Disorders

SummaryWe present the construction of an expert system (ES) for the diagnosis of Obsessive-Compulsive Disorders (OCD). It concerns an artificial intelligence tool, in Lisp language compatible with any personal computer (PC) with a hard disk. The ES asks the user 50 questions in natural language, on the patient or on a clinical history. It is provided with 115 rules of reasoning. Using single or multivaluate variables, the ES reaches the diagnosis of the Obsessive-Compulsive Disorders or the recommendations of differential diagnosis with related patterns or involucred with obsessive pathology: phobic, affective, schizophrenic and Gilles de la Tourette disorders. Finally, the perspectives for the utilisation of the ES in psychopathology are disscussed, in conjunction with the 2 serious problems created, design difficulty and user acceptance.

An Expert System for Differential Diagnosis of Myocardial Infarction

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Abdul Jaleel ◽  
Reza Tafreshi ◽  
Leyla Tafreshi
Keyword(s):  
Myocardial Infarction ◽  
Differential Diagnosis ◽  
Expert System ◽  
Search Algorithm ◽  
Reliable Performance ◽  
Rule Sets ◽  
Rule Set ◽  
Ectopic Beats ◽  
Electrocardiogram Ecg ◽  
Algorithmic Rule

Automated early detection of myocardial infarction (MI) has been long studied for the purpose of saving human lives. In this paper, we propose a rule-based expert system to analyze a 12-lead electrocardiogram (ECG) for various types of MI. This system is developed by mapping clinical definitions of different types of MI and their differential diagnosis into corresponding algorithmic rule sets. Essential preprocessing steps such as baseline correction, removal of ectopic beats, and median filtering are carried out on recorded ECG. Techniques such as multistage polynomial correction and QRS subtraction are exploited to achieve reliable preprocessing. The processed ECG is then delineated using a time-domain differential-based search algorithm recently proposed by the team to obtain the relevant features and measures. These features and measures are further utilized by an if-then rule set to classify the ECG into various groups. The performance of the system when validated on sample MI database exhibited a sensitivity of 95.7% and specificity of 94.6%. Unlike many previous works, this reliable performance is achieved without the use of abstract classifiers or the need of prior training. Being based on medical definitions, the system is also easily comprehensible, modifiable, and compatible with manual diagnosis.

Using ILIAD system shell to create an expert system for differential diagnosis of renal masses

1993 ◽  
Vol 17 (5) ◽  
pp. 289-297 ◽  
Author(s):  
Phei Lang Chang ◽  
Yu-Chuan Li ◽  
Chi Ju Wu ◽  
Ming Hsiung Huang
Keyword(s):  
Differential Diagnosis ◽  
Expert System ◽  
Renal Masses

scholarly journals Sistem Pakar Menggunakan Metode Certainty Factor dalam Identifikasi Pengembangan Minat dan Bakat Khusus pada Siswa (Studi Kasus di SMA Muhammadiyah 1 Padang)

2020 ◽  
Author(s):  
Nandra Sunaryo ◽  
Yuhandri Yunus ◽  
S Sumijan
Keyword(s):  
Expert System ◽  
Test Data ◽  
System Application ◽  
Special Interests ◽  
Certainty Factor ◽  
Good Level ◽  
Factor Method ◽  
Guidance Teacher

Identification of the development of special interests and talents needs to be done in order to find out the potential of students. This knowledge is needed by the teacher when providing counseling guidance to students in order to know the types of special interests and talents of students. This study aims to identify the development of special interests and talents in students based on the characteristics of special interests and talents appropriately. In this study using the Certainty Factor method where this expert system can assist experts in identifying the development of special interests and talents based on the characteristics of special interests and talents in students. Followed by calculating the level of accuracy with the results of the counseling guidance teacher analysis. The results of the testing of the Certainty Factor method were successfully applied by comparing the data with the system that had been designed so that a good level of accuracy was obtained from the results of the system calculations with expert decisions of 80% of the 5 test data. This expert system application can be used as an alternative in identifying the development of special interests and talents in students.

scholarly journals Sistem Pakar dalam Mengidentifikasi Minat Vokasi Menggunakan Metode Certainty Factor dan Forward Chaining

2020 ◽  
Author(s):  
Kurniawan Jefdy ◽  
Sarjon Defit ◽  
Yuhandri Yunus
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Expert System ◽  
Test Data ◽  
System Application ◽  
Vocational Interests ◽  
Personality Types ◽  
Certainty Factor ◽  
Continuing Higher Education ◽  
Vocational Interest

Developing an expert system application in providing an overview of the interests of students to help decision making interests in the vocational field so that they are right on target in choosing a major. In this study, using the Certainty Factor method and the Fordward Chaining method where this expert system can help experts identify vocational interests based on the characteristics of vocational interest in students. The personality types used to determine the type of vocational interest are Tangible, Thinking, Flexible, and Entrepreneur. The results of system calculations with expert decisions are worth 80% of the 4 test data, so a good level of accuracy is obtained. The resulting expert system can help students quickly provide an overview of vocational interest in making department decisions in continuing higher education, can carry out online consultations, document files, and can be used as a consultation portal for students.

A User-oriented Approach to Decision Making in Differential Diagnosis and Therapeutic Advice

1986 ◽  
Vol 25 (04) ◽  
pp. 199-206 ◽  
Author(s):  
N.G. Rao ◽  
B.E. Prasad ◽  
S.K. Guha ◽  
P.G. Reddy
Keyword(s):  
Differential Diagnosis ◽  
Expert System ◽  
Computer Assisted ◽  
Medical Expert ◽  
Simple Method ◽  
Natural Behaviour ◽  
Suitable Treatment ◽  
Methods Of Treatment ◽  
And Training ◽  
Selection Of

SummaryA search for a user-oriented, computer-assisted expert system resulted in a simple method of simulation of a human consultant’s thinking and action. Making a quick decision in differential diagnosis and selection of the proper line of treatment when faced with a problematic patient has always been a challenging task for clinicians, especially those working in emergency wards, intensive care wards and acute wards. Consulting a vast memory of a number of past cases of a similar nature is near to the natural behaviour of a medical expert and well acceptable to most of the users and patients alike. In this method, the case details of an undiagnosed fresh patient are compared with the details of similar cases diagnosed and treated earlier by experienced consultants and stored in the computer memory. These old cases with confirmed final diagnoses, proven methods of treatment and correctly recorded outcomes enable the system to arrive at the most likely diagnosis, the next likely diagnosis and the most suitable treatment for the new case.A check facility to ensure the accuracy of the decisions made by MEMCONSULT through comparison with a textbook description and any particular old case in the memory has been added in view of the education and training of newcomers in the field of medicine. A self revising capability depending on the changed clinical condition of the patient is also implemented to suit the basic requirements of an ideal expert system in medicine.

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