Fish disease diagnosis program — problems
and some solutions
Daniel Zeldis
a,*, Shawn Prescott
baZeldis Industries Ltd. (Fish-Vet Israel),POB 3521,Eilat 88134, Israel bFish-Vet Inc.,12620 I
6y Mill Rd.,Reisterstown,MD 21136, USA
Received 4 December 1998; accepted 17 August 1999
Abstract
Any software dealing with disease diagnosis has to overcome various problems. Some are inherent in the diagnostic technique, others arise because of the specific problem domain. We have evaluated different expert-system technologies including neural-nets, case-based expert systems (ES), rule-based ES and fuzzy logic. The problem domain (fish disease) has it’s own problems, the major one being that there is no accepted database of cases like there is in other medical fields. This precludes the use of diagnostic techniques needing a large number of test cases. The other problem in this context is the effort to deal with ALL diseases for multiple species. We explore the different ES techniques, and outline the final product (Fish-Vet) which includes a hybrid system that enables us to obtain reasonable diagnoses in a timely manner. This program uses elements of fuzzy, rule-based and statistical systems. The mix and match approach proved useful, and further work has to be performed in order to incorporate other artificial intelligence techniques into the process. © 2000 Elsevier Science B.V. All rights reserved.
Keywords:Expert systems; Diagnosis; Fish disease; Fuzzy logic; Rule based systems
www.elsevier.nl/locate/aqua-online
1. Introduction
Fish-Vet is a software program for diagnosis of fish disease. As such, it has to deal with more than one species, and a multitude of diseases. The diseases themselves result from nutritional and environmental problems as well as infections
* Corresponding author. Tel.: +972-7-6379756; fax:+972-7-6337278. E-mail address:danzel@inter.net.il (D. Zeldis)
by parasites, viruses, bacteria and fungal agents (Post, 1983; Stoskopf, 1993). All of this make for a huge problem space, and a major challenge for anyone trying to develop a program that will reach reasonable and timely diagnoses.
Most of the existing examples of disease diagnosis programs are in the field of human medicine. Most of them tackle only a single disease or a subset of related diseases and none (to our knowledge) attempts to diagnose all human diseases.
During the development of Fish-Vet, we looked into rule-based, case-based, neural-net and fuzzy logic systems. Each of these ‘pure’ systems has advantages as well as deficiencies. Our decision was to create a working program and, where real-world constraints were in conflict with the ‘pure’ systems, we ‘polluted’ that
system as long as the end result was faster and/or more accurate.
2. General problems in fish disease diagnosis
There are several problems inherent in a disease diagnostic process. These have to be taken into account by any software package trying to aid the diagnosis.
No disease exhibits all the signs described in the literature. In most cases there
are acute and chronic phases of a disease having differing signs. Therefore, the program has to be able to reach the right diagnosis with a partial set of signs.
There is a time progression for every disease. A disease seen when the first
clinical signs appear will exhibit different signs than when mortalities are already occurring.
Since the program has to obtain input from a human user, the problem of
terminology looms large. Until today and despite efforts made by international organizations, no accepted vocabulary has been agreed on for veterinary termi-nology (CAP, 1998; HL7, 1999). This is now changing with the incorporation of veterinary terminology in SNOMED. Moreover, cultural differences will also result in different terms being used for the same condition.
In many cases, by the time the fish exhibit signs of a problem, there is already
a secondary agent involved (virus+bacteria, fungus+bacteria, etc.). Therefore
the signs observed by the user may ‘belong’ to more than one disease in the program’s database.
‘It is human to err’, but never more so than in our case. We have to take into
account that the signs chosen by a user to describe a condition are influenced by his knowledge and experience. Therefore we have to deal with the possibility that ‘wrong’ signs will be entered by the user.
3. Rule-based systems
After the database is ready, we can query the rules using an inference engine (IE), which is at the heart of the ES. We present the IE with observed signs, and it will search the rules, looking for those which fit the observations. Using those rules, the IE will try to find diseases whose rules have ‘fired’. Going the other way, using a list of possible diseases, the IE will enable us to see other rules and define other observations needed to make the differential diagnosis.
The advantages of this technique are that it is mature, there are many systems to choose from, the system can ‘explain’ it’s results (by showing those rules used to reach an answer) and rules can be edited so that the IE is tuned to obtain better results. Another strength is that in most cases the IE uses an existing ES-shell so the development will not entail extensive programming as such, only the entry of rules (Tu´nez et al., 1996).
The main deficiency of rule-based systems is that they require some ‘deep’ knowledge in order to be truly effective. This means we need to know the causal relationships between signs and disease. Another problem is that the rule database grows very rapidly as the problem space expands. As a result, the computation time grows and for the problem space we needed, it became totally impractical.
In Fish-Vet we use a rule based system with a very limited set of rules, whose only purpose is to cut down the problem space to a manageable size quickly. This entails rules for species membership (i.e. some diseases are species-specific) so that for any diagnosis we can ‘throw out’ all species-specific diseases not belonging to the species in question. A similar approach is used for water type (i.e. seawater specific diseases can be disregarded when looking at a freshwater fish), etc.
4. Case based reasoning (CBR)
This method uses the storage of a large number of previously solved cases in some normalized form. When the user presents a new case to the system, it searches the database, locates ‘similar’ cases and presents them to the user. By looking at those similarities, the user is able to formulate a presumptive diagnosis and is guided toward further data collection in order to narrow the list of possible problems (Althoff et al., 1998).
The main advantages of this method are that systems become better as more cases are entered into the database, the program has the ability to explain it’s decisions and the fact that there is no need for programming (Evans and Winter, 1995).
5. Neural networks (NN)
Neural networks (NN) are a computer ‘simulation’ of the interconnected neurons in our brains. This method simulates a network of interconnected neurons usually arranged in three layers, where the lowest one receives inputs and the top signals the outputs. Work with a NN starts with a ‘clean’ network and the developer has to ‘train’ it to recognize a specific set of inputs.
Training consists of presenting the NN with a series of cases and providing feedback as to the desired output. During the training the NN adjusts the ‘weights’ given to each input neuron till the NN will give the desired output (i.e. until the NN learns to recognize this set of inputs; Kappen, 1996).
The advantages of this method lie in the fact that the network ‘learns’ by itself so that there is no need for a priori knowledge. Therefore, to obtain to the right output a NN can often find relationships of which we were not aware (Babic et al., 1995; Smith et al., 1996).
The problems with NN are that there is a need for a large number of test cases for each disease of interest and that the NN cannot ‘explain’ it’s results. As discussed above, there is a dearth of well documented test cases to use in training the NN, and the ability to explain results is critical in the context of disease diagnosis. Therefore, we could not use NN in Fish-Vet.
6. Fuzzy logic systems (FL)
Fuzzy logic utilizes a many-valued form of logic. Unlike the ‘crisp’ logic of a yes-no system, a FL system has also in-between values. This allows us to describe a point as a function of it’s membership in different sets. For example, if we define a ‘cold water’ range of temperatures and a ‘hot water’ set, any value can belong to one, the other or both, (e.g. 24°C is a 10% member of the cold water group and 90% in the hot water one, while 8°C is 100% in cold water group and 30°C 100% in the hot water one).
Most of the applications of FL have been in the control field. FL controllers are embedded in everything from washing machines to locomotives. In the last few years applications in the domain of disease diagnosis have started to proliferate (Fathi-Torbaghan and Meyer, 1994; Bellamy, 1997).
In our case, we decided to use the idea of FL, if not the technique itself. This was performed by creating membership functions for signs, as explained later.
7. The Fish-Vet diagnostic process
The diagnostic process is iterative and includes the following steps.
Choosing species, water type and all signs observed in the affected fish (Fig. 1).
A diagnosis is run and results in a list of candidate diseases presented to the user.
Each disease in the list is given a ‘magic number’ which defines it’s ‘distance’ from the most likely disease. This most likely disease is always given a value of 1.0 and all other numbers are normalized accordingly (Fig. 2).
The user is shown all the signs ‘expected’ for the different diseases in the list, and he reexamines his fish looking for any of these candidate signs.
The new signs are added and a diagnosis run again.
After 2 – 3 iterations the difference between the most likely disease and it’s closest neighbor should be large enough for us to be confident that the first disease is the right one.
The Fish-Vet diagnostic engine includes the following elements.
A small rule base for species, water type, etc. This is used mainly to confine the problem space and cut the number of candidate diseases entering the second stage. A list of candidate diseases is formed by choosing only diseases prevalent in the specified water type, non species specific diseases and diseases specific to the chosen species.
A fuzzy logic module that expands the list of chosen signs using membership functions for the defined sets of signs. For example, there is a ‘red on skin’ set including all signs from redness on skin up to hemorrhages. This means that even
Fig. 2. List of candidate diseases arranged by likelihood.
when the user chooses ‘petechiae’, the program will include in the list of signs to check also ‘subcutaneous hemorrhage’, ‘red skin’, etc., each at a weight corre-sponding to their distance from the original sign in the set. Therefore even if the user, because of inexperience, uses the ‘wrong’ term, the program will still be able to reach the ‘right’ diagnosis. This will also help with the time progression problem. Redness on skin in day 1 of an outbreak may well turn into hemor-rhage on day 4. Using sets allows us to obtain the same diagnostic even when sampling at different stages of the disease.
A clustering module that searches for diseases whose signs match the sign vector chosen by the user. Every disease in the database is coded as a multidimensional vector of it’s signs.
A module that gives weights to the different signs according to their relevance to the different candidate diseases, i.e. some signs are more indicative of specific diseases.
Specific signs are given more weight than general ones (e.g. ‘loss of appetite’ is a general sign while ‘Kyphosis’ is a very specific one).
A top-ten selection after computing the scores for all candidates, which is presented to the user after normalizing the scores.
8. Discussion
During the last 5 years Fish-Vet has been deployed worldwide in various settings. It has proven itself as an aid to diagnosis, a valuable companion for the field veterinarian and as an educational aid in veterinary schools.
The main difficulties with the program can be divided into external and inter-nal. External problems include the fact that, unlike terrestrial animal diseases, the whole fish disease field is in a state of constant flux. New diseases are discovered continuously as new species are commercially developed and old spe-cies are raised in new geographic areas. These problems can be overcome by ongoing updating of the information in the program.
Another problem arises from the fact that, especially in aquaculture settings, we are dealing with a large number of animals. Individual animals will probably exhibit different stages of one or more diseases at the same time. Therefore, sampling is essential to reaching a diagnosis.
Internal problems are a result of deficiencies in the diagnostic engine itself. It is clear that we cannot hope to easily achieve best-of-breed results across the board. Any program limiting itself to a subset of problems can conceivably reach better coverage and accuracy. This has to be weighted against the advan-tage of having a single program to use for any situation. An added problem with partial implementations is that they require some pre-diagnosis processing in order to decide whether they are suitable for the problem at hand. This precludes their use in any non-expert setting.
The development of Fish-Vet has shown us that we can reach our aims by using a ‘mix-and-match’ approach, foregoing commitment to any one technique. We believe that in the future we will see expanding use of hybrid systems, a trend that has already started. This allows the creation of programs with the advantages of all methods and almost none of the deficiencies (Molnar et al., 1993; Ikeda, 1996; Lopez and Plaza, 1997; Reategui et al., 1997).
Within the Fish-Vet program there are two other modules not discussed here. One is a bacterial identification module (using biochemical profiles) and the other a parasite identification module. Both should be used to confirm and support the diagnostic process, especially because at times this may be the only way to reach a diagnosis at all.
9. Conclusion
One major facility lacking in the field of fish disease is a database of test cases. This hinders further development of some methods discussed above. Such a database could also be used to tune and perfect other diagnostic programs and will enable research into their efficacy, quality and accuracy (Berner et al., 1996). We strongly believe such a database would enable many advances and will support such an effort (Puppe et al., 1995).
Our field is less constrained than human medicine, where legal problems hinder the deployment of decision support programs. This should be looked at as an opportunity to develop truly innovative diagnostic programs, proving concepts that may be transferred later into human and other veterinary medicinal settings.
Acknowledgements
The most important element of Fish-Vet or any other diagnostic program is the information and knowledge embedded in it. In this paper we have concentrated on the technical aspects of developing it. But, this would still be an empty shell without the efforts poured into it by more than 40 experts from 17 countries. This group has enabled us to obtain a product with a level of credibility we could never hope to achieve alone. We thank you all. A special mention should go to Mr Kent Hauck (Utah Department of Aquaculture, Salt Lake City) whose efforts in our behalf never cease to amaze. Lastly, we would like to mention Dr’s Shree Nath and Doug Ernst without whose efforts (and constant reminders) this paper would still be a gleam in our mind’s eye.
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