Intelligent Systems to Assist in Cytological Diagnosis and to Train Cytotechnics TIN

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1 Jornadas de Seguimiento de Proyectos, 2007 Programa Nacional de Tecnologías Informáticas Intelligent Systems to Assist in Cytological Diagnosis and to Train Cytotechnics TIN Jose Manuel Zurita López * Leading Researcher Dept. Computer Science and Artificial Intelligence University of Granada Abstract This project aims at the application of Soft Computing techniques to the development of intelligent systems which assist in the cytological diagnosis and the training of cytotechnics. As an important previous task, suitable knowledge representation must be defined for the Cytology field. Then knowledge extraction methods must be defined for both acquiring knowledge from the experts and deriving knowledge from a set of clinical data. To achieve all of this, different components from Soft Computing will be used (Artificial neural networks, Fuzzy Systems, Evolutionary systems). Later on, two intelligent systems to aid in cytological diagnosis will be developed: one for the thyroid and another for cervix-vaginal cytologies. These systems will not only allow for the management and exploitation of knowledge, but also will be able to learn and hence increase their knowledge base. They will also support the diagnoses with bibliographic references relative to them and retrieved through bibliographic database queries. As a specific case of exploitation of these systems, an intelligent tutoring system to assist in the training of future cytotechnics will be developed. Keywords: Intelligent systems, cytological diagnosis, Soft Computing, knowledge acquisition. 1 Objectives of the project Cytological diagnosis is a very important problem in the detection of a number of pathologies (e.g. cancer). It is a basic task which requires wide knowledge and experience to be developed in an effective way. Most of observations and decisions in cytology are tainted by uncertainty and vagueness. The interpretation of morphological features observed through the microscope is fundamental in the diagnosis process. In turn, the difficulty of the diagnosis depends on the kind of tissues, where some soft ones are rather straightforward to other where available knowledge is not complete. Then, cases appear commonly where the cytologist has to consult bibliographic references looking for assistance. *

2 This field represents an area for the design of intelligent systems which require the representation and management of knowledge extracted from experts, but also require some other techniques to automatically extract knowledge, techniques like Machine Learning. In addition, the system should retrieve information from bibliographic reference databases. A good cytologist or cytotechnic needs a rather wide set of knowledge. Their training means an important effort. This effort can be alleviated and improved by using an intelligent system illustrating real cases, explaining the obtained diagnosis and providing references to justify and extend the answer. A system like this falls in the category of Intelligent Tutoring Systems, which is a rather active area of research to improve training and learning in human beings. After the issues discussed above, the general goals for the current project are: a) To study the problem of knowledge extraction for cytological diagnosis and provide solutions especially suited for it. These solutions must cover both knowledge extracted from experts and knowledge drawn from sets of clinic case data. b) To design a system based on Soft Computing techniques which can assist professionals in their daily task of cytological diagnosis. This system should allow for an easy way to include further knowledge or update the one already available. It must be accessible through web so that experts all over the world can use it and collaborate in the construction of its knowledge base. This system will also include bibliographic reference queries which will be fired after a given diagnosis has been reached. The structure of the system will be general enough so that it can be adapted to a number of pathologies. In particular, two specific systems will be built: one devoted to thyroid and another for cervix-vaginal pathologies. c) To build an intelligent tutoring system associated to the intelligent system aimed at the improvement of cytotechnics training. Starting from the knowledge embedded in the intelligent system and endowed with a wide set of cases built, this will be a consult and training system for future cytologists. These goals will be reached after executing the following work plan, which is divided into seven task. The planning along with its chronogram is detailed below. Task 1. Analysis of the cytological diagnosis problem. Task 2. Design of the intelligent system model. Task 3. Knowledge acquisition. Task 4. Development of the intelligent systems. Task 5. Analysis of the cytotechnic training problem. Task 6. Design of the intelligent tutor system. Task 7. Development of the tutor system.

3 Jornadas de Seguimiento de Proyectos, 2007 Programa Nacional de Tecnologías Informáticas Activities/Tasks Task 1.1 General analysis of the problem Task 1.2 Requirement analysis for the intelligent system Task 2.1 Architectural design Task 2.2 Module of knowledge representation Task 2.3 Module of inference Task 2.4 Module of result justification Task 2.5 Module of learning Task 2.6 Module of bibliography queries Task 3.1 Study, design and application of knowledge extraction techniques Task 3.2 Application of machine learning methods to clinic case databases Task 3.3 Diagnosis and feature selection (Thyroid) Task 3.3 Diagnosis and feature selection (Cervix-vaginal) Task 3.4 Keyword selection (Thyroid) Task 3.4 Keyword selection (Cervix-vaginal) Task 3.5 Image database creation (Thyroid) Task 3.5 Image database creation (Cervix-vaginal) Task 4.1 Intelligent system coding Task 4.2 Integration and test Task 4.3 Validation Task 5.1 Analysis of teaching requirements (ITS) Task 5.2 Functional and non-functional requirement analysis (ITS) Task 6.1 Architectural design (ITS) Task 6.2 Case definition Task 6.3 Evaluation module Task 6.4 Student management module Task 7.1 Coding (ITS) Task 7.2 Integration and test (ITS) Task 7.3 Validation (ITS) First year (*) Second year (*) Third year (*)

4 Jornadas de Seguimiento de Proyectos, 2007 Programa Nacional de Tecnologías Informáticas 2 Success level reached in the project According to the tasks planned in the chronogram above, the degree of fulfilment of the project is as follows: Task 1.1 General analysis of the problem. Months 1 to 4 inclusive. During these months, several meetings were organized at the Hospital Virgen de las Nieves in Granada. In those meetings a general analysis of the problem from the different point of views of the group components: knowledge engineers, medicine doctors, and bibliography experts. A great effort was done to combine different perspectives. These meetings were successful to the point that we acquired a general view of the problem and the communication among different disciplines was improved. Of course, along these four months previous work in the line of research of the project was reviewed. Task 1.2 Requirement analysis for the intelligent system. Months 4 to 6 inclusive. In parallel with Task 1.1, a requirement analysis was performed. For these three months, a number of requested features were gathered. For example, an important requirement to widen its accessibility was that the intelligent system could be used through the Internet. The whole process was finally summarized in the Requirement Specification Document. Task 2.1 Architectural Design. Months 7 to 8 inclusive. This task implied the bird-eye design of the main system modules, including their inputs and outputs as well as their interfaces to modules with which each of them interacted. Task 2.2 Module of knowledge representation. Months 9 to 10 inclusive. After a detailed study of the kind of knowledge and inference processes, we seek for the best suited knowledge representation. The final choice was rules. The system would be based on production rules. Then, since one of the main requirements was the management of uncertainty, we analyzed the kind of uncertainty present in this knowledge and found fuzzy rules to be an appropriate model to cope with it. Task 2.3 Module of inference. Months 11 to 12 inclusive. To infer new knowledge with fuzzy rules and/or facts, approximate reasoning has to be used. This is the natural inference procedure within Fuzzy Logic. The combination of fuzzy rules and approximate reasoning allows coping, managing and, processing most kinds of uncertainty arising in real world problems. Task 2.4 Module of result justification. Months 9 to 10 inclusive. A key issue in every expert system is the justification offered for every final answer, because the trust the user will deposit on the system depends on the reasoning process and then on the justification for the followed path. The use of rules as the representation brick of knowledge would certainly help in the task of justifying the inferences, but it is still necessary to develop it.

5 Task 2.5 Module of learning. Months 10 to 12 inclusive. This module was conceived with the aim of endowing the intelligent system with a longer cycle of life, actually, a cycle in continuous development. The system should add new knowledge which could arise along the life of the system. In particular, new knowledge coming from the inference processes carried out by the system and their a posteriori validation should be included. Task 2.6. Module of bibliographic queries. Months 12 to 14 inclusive. A common situation among medicine doctors using intelligent systems arise when they want to widen the knowledge on the final diagnosis. A hard work has been carried out by bibliographic experts working along with doctors to define the main queries of interest for anatomo-pathologists. These queries have been formulated in a number of query languages. When a diagnosis is reached, the system may produce a set of bibliographic references related to it, by performing the queries after the user request. The system adapts the general query to the particular interests of the doctor, performs the queries and integrates the results. Task 3.1 Study, design and application of expert knowledge extraction techniques. Months 7 to 10 inclusive. Knowledge extraction has been performed through three ways: a) Structured interviews. Knowledge engineers use pre-elaborated interviews to request information from experts. Then the answers are evaluated to build the knowledge base. b) Web. In order to consider a wider and more complete knowledge base, we developed a web application through which interested domain experts could provide new information regarding features, diagnoses, relationships among them, etc. This application was publicized in a number of conferences. c) ADAUCO tool. ADAUCO is a software prototype developed after a Ph.D. Thesis entitled Design and implementation of a tool for automatic knowledge acquisition. This thesis was written by Dr. José Jesús Castro, a member of the research team. The theoretical foundations for the thesis has been published in prestigious international journals [2],[3]. Task 3.2 Application of learning procedures on clinic datasets. Months 7 to 13 inclusive. This task was performed in parallel with task 3.1. In this case, we focused on automatic knowledge extraction through the application of Machine Learning procedures. These methods induced fuzzy rules out of the data gathered in clinic databases. The considered methods include [1], Wang & Mendel s procedure and others proposed in [8] which use different models of artificial neural networks for the construction or derivation of fuzzy rules representing the knowledge underlying in a set of data. These methods produced a bunch of rules which increase the knowledge in the base. However, before their final acceptance into the knowledge base they have to be tuned and validated. Task 3.3.a Diagnosis and feature selection (thyroid). Months 10 to 11 inclusive. This task was carried out applying the knowledge extraction techniques described above. The process was documented in [4]. We also considered, an alternative knowledge representation, intended for a wider diffusion of the knowledge: the use of ontologies. The results of this initiative were published in [5].

6 Task 3.3.b Diagnosis and feature selection (cervix-vaginal). Months 11 to 14 inclusive. The considered features and diagnosis for the diagnosis of cervix-vaginal pathologies are described in [6]. Anyway, this knowledge base can be augmented in any moment through new contributions by experts. Task 3.4 Key word selection. Months 12 to 16 inclusive. The results for this task are two collections of key words for the pathologies of interest. These key words are essential in the formulation of bibliographic queries and for the description of knowledge. Task 3.5 Image base creation. Months 14 to 22 inclusive. The intelligent system to develop interacts with user in a visual intensive way. The images of samples under the microscope are the main source of information. The availability of a set of high quality and representative images is essential for the system. These images are a key component of the knowledge base. The caption of these images is an arduous task. Some setbacks due to the availability of samples and troubles with the capturing equipment have produced a delay in its execution. The task is not complete yet, although its final is very close. Task 4.1. Intelligent system coding. Months 13 to 20 inclusive. The first intelligent system targeted to the assistance in the diagnosis of thyroid pathologies is finished (and available through the Internet). It has been developed using only Open Source tools, such as Apache, MySQL, PHP and Javascript. This work has uncovered some weak points in the development methodology. For the second system, we have changed the development philosophy to prototype-based cycle of live. In particular, we have migrated to the python programming language along with the web development framework, Django. This has implied a slight delay in starting up the second system (for cervix-vaginal pathologies) implementation, but it is advancing at a higher pace, so we are confident that the whole implementation will be available in the first months of the third year. Task 4.2 Integration and test. Months 21 to 22 inclusive. This task is completely finished for the first system (thyroid). As was pointed out in the task above it is delayed for the second system. The change in the development methodology will also accelerate this task because the integration and test stages are intertwined in the development process (inherited from the Xtreme Programming methodology). Task 4.3 Validation. Months 23 to 24 inclusive. These two tasks are delayed due to their dependence on the two above. Task 5.1 Analysis of teaching requirements (for the ITS). Months 17 to 18 inclusive. The experts in cytotechnic training produced a report gathering the teaching requirements necessary for the formation of these professionals. This report includes a description of the weak points of current methods pointing out several lines of possible improvement through their automation.

7 Tasks 5.2 Functional and non functional requirement analysis. Months 19 to 20 inclusive. Through the application of the combined methodology of the waterfall and prototype-based models, functional and non functional requirements for the ITS were extracted. This software appliance has two especial features: its teaching orientation and the use of expert knowledge to guide the learning of the student. These two peculiarities affect the way in which requirements are captured. The adaptation of the methodology for these points has delayed the conclusion of this task and propagated the finalization of the former and this task, and all of those depending on them. Tasks 6.1 to 6.3. ITS development. All of these tasks have been delayed due to the necessary adaptation of requirement extraction methodology to the specific features of the ITS application. 3 Result indicators The indicators showing the relevance of results obtained so far are fairly promising. In particular, there following papers and communications have been published as a result of the work developed in this project: a) J. Esquivias, J. Alonso, J.M. Benítez, M. Parra, F. Jiménez, C. Prieto, M. Merino. El diagnóstico en citología: Extracción de rasgos y cuantificación de los mismos. Ensayo sobre citología tiroidea, VII Congreso Virtual Hispanoamericano de Anatomía Patológica. b) J. Esquivias, J.M. Zurita, M. Gómez, E. Villar, J.M. Sánchez, J. Alonso. Representación del conocimiento mediante ontologías: Ensayo sobre citología tiroidea, VII Congreso Virtual Hispanoamericano de Anatomía Patológica. c) J. Esquivias, M.M. Serrano, J. Alonso, M. Parra, C. Nieves, C. Prieto, M. Merino, J.M. Benítez, C. Herrera. Bases para la construcción de un sistema tutor inteligente para aprendizaje de la citología cérvico-vaginal, VII Congreso Virtual Hispanoamericano de Anatomía Patológica. d) M. Rodíguez del Castillo, J.M. Benítez, J.M. Fernández-Luna, J.M. Zurita, C. Herrera, F. Jiménez Burgos, M.M. Serrano, J. Merino, J. Esquivias. Motores de búsqueda automáticos a partir de un diagnóstico determinado, VII Congreso Virtual Hispanoamericano de Anatomía Patológica. With regard to developments we should make the following remarks: a) The intelligent system to assist in thyroid diagnosis is completed finished, pending only on its validation. This application has already been tested by a number of national experts which have stated their positive opinion with respect to the performance of the tool. The presentation of the system in several conferences has been welcomed. b) The system for cervix-vaginal pathologies is in the final implementation-integration-test stage, all of them jointly due to the development methodology used in this case. The delay in the start of the implementation is being compensated by an accelerated pace (and safest, too) coding. In short, it will be available for its validation by a new group of experts.

8 c) The intelligent tutoring system for cytotechnics has already overcome the specification stage. Its conclusion is expected by the third quarter of the final year. With regard to technology transfer and collaboration with other research groups, this project has yielded an intelligent system architecture which can be readily adapted to other areas in Medicine. Starting from this project and in collaboration with other medicine doctors, a new initiative sprang, The virtual patient project. This project is been developed by a group of researchers from the University of Granada (three of them are already involved in the research of the current project), the IAVANTE Foundation (in Spanish, Fundación para el Avance Tecnológico y Entrenamiento Profesional) and CITIC (in Spanish, Centro Andaluz de Innovación y Tecnologías de la Información y las Comunicaciones). 4 Bibliography [1] J.L. Castro, J.J. Castro-Schez, J.M. Zurita. Use of a fuzzy machine learning technique in the knowledge acquisition process, 2001, Fuzzy Sets and Systems, 123, pp [2] J.L. Castro, J.J. Castro-Schez, J.M. Zurita. Combining both a fuzzy inductive learning and a fuzzy repertory grid method, 2002, en Technologies for constructing intelligent systems 1, Task, B. Bouchon-Meunier, J. Gutiérrez-Ríos, L. Magdalena, R.R. Yager (editores). [3] J.J. Castro-Schez, J.L. Castro, J.M. Zurita. Fuzzy repertory table: A method for acquiring knowledge about input variables to machine learning algorithm, IEEE Transactions on Fuzzy Systems, vol. 12, nº 1, pp [4] J. Esquivias, J. Alonso, J.M. Benítez, M. Parra, F. Jiménez, C. Prieto, M. Merino. El diagnóstico en citología: Extracción de rasgos y cuantificación de los mismos. Ensayo sobre citología tiroidea, VII Congreso Virtual Hispanoamericano de Anatomía Patológica. [5] J. Esquivias, J.M. Zurita, M. Gómez, E. Villar, J.M. Sánchez, J. Alonso. Representación del conocimiento mediante ontologías: Ensayo sobre citología tiroidea, VII Congreso Virtual Hispanoamericano de Anatomía Patológica. [6] J. Esquivias, M.M. Serrano, J. Alonso, M. Parra, C. Nieves, C. Prieto, M. Merino, J.M. Benítez, C. Herrera. Bases para la construcción de un sistema tutor inteligente para aprendizaje de la citología cervico-vaginal, VII Congreso Virtual Hispanoamericano de Anatomía Patológica. [7] M. Rodíguez del Castillo, J.M. Benítez, J.M. Fernández-Luna, J.M. Zurita, C. Herrera, F. Jiménez Burgos, M.M. Serrano, J. Merino, J. Esquivias. Motores de búsqueda automáticos a partir de un diagnóstico determinado, th World Conference on Heath Information and Libraries (ICML9). Salvador de Bahía, Brasil, Septiembre [8] J.M. Benítez. Extracción de reglas difusas de y con redes neuronales artificiales, Tesis Doctoral, Universidad de Granada, 1998.

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