An algorithm to identify the Kennedy Classes in partial reduced

edentulism

CRISTINA GENA DASCĂLU1, COSMIN ONICIUC2, GEORGETA ZEGAN3 1Medical Informatics and Biostatistics Department

2Dental Prosthetics Department, 3Orthodontics Department University of Medicine and Pharmacy ”Gr.T.Popa” - Iași

Str. Univesității no. 16, IAȘI, 700115 ROMANIA

cdascalu_info@yahoo.com, cgdascalu@mail.umfiasi.ro Abstract: - The Kennedy classes identification plays an important role in finding the oral diagnosis and the therapeutically solutions in the area of partial reduced edentulism. The physician doesn’t always need to record the Kennedy classification in the computerized patient’s file. Therefore, we achieved an algorithm in Visual FoxPro which, starting from the diagnosis on teeth recorded in a database, establishes the Kennedy class for each patient, as well as the frequencies distribution for the Kennedy classification on the whole database. The algorithm was written in order to be used on databases that contain the dental image using the WHO model established in the WHO Center from our faculty. The advantage of this model was that it allowed to record in a very precisely and refined manner the diagnosis at the level of each teeth, without further analysis. Based on this model, our algorithm allows detailing these analyses, and finds supplementary information for the patient as well as for the whole database. The using of this algorithm proved to be very useful, especially on large databases designed mainly for clinical purposes.

Key-Words: - partially reduced edentulism, Kennedy classes, algorithm

1 Introduction In the modern dentistry, the patients’ computerized evidence represents a necessity, being the most direct way to obtain large databases with medical data, recorded in a correct, clear and complete way and already prepared for further scientific researches. In usual practice, the dentistry physician doesn’t always need to record some specific diagnosis aspects in the computerized patient’s file – the reasons for this situation being very realistic – from the simple lack of time, until at more complex causes, like those economic and financial. This problem is in fact not very serious, and it doesn’t represent a major drawback for the dentistry research, but only if a simple requirement is fulfilled: the databases with patients’ data must be well designed, and the data must be recorded in such a way to allow their future transformation and processing in order to obtain other types of information.

One of the most important requirements in the stomatological practice, as well as in the research, is to record, as exactly as possible, the patient’s diagnosis on each tooth. The teeth are identified

uniquely by numbers, using two major encoding systems: - a

system developed by the FDI World Dental Federation in 1971, and called “the FDI Two-Digit Notation”, or the ISO-3950 notation; this system, recognized worldwide, is used especially by the physicians in Europe (figure 1a, b, [1]);

- another system, called “the Universal Numbering System”, is promoted by the American Dental Association, being used primarily by most general dentists in the United States (figure 2a, b, [1]).

Using these codes, the most Electronic Health Records developed for dentistry have a special section for the so-called “teeth chart”, usually designed as a picture or a table, which allows to the physician to fill quickly and precise the patient’s diagnosis and treatment plan using color codes and a few other very simple and intuitive graphical symbols (figure 3, 4). This diagnosis regards not only the odontal situation and the prosthetics requirements, but also the periodontal situation,

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necessary for more complex treatments, as the dental implants.

Figure 1a. The FDI Two-Digit Notation

(http://en.wikipedia.org)

Figure 1b. The FDI Teeth Chart

(http://users.forthnet.gr)

Figure 2a. The Universal Numbering System

(http://en.wikipedia.org)

Figure 2b. The Universal Numbering System Chart

(http://users.forthnet.gr)

Figure 3. Example of Electronic Dental Record:

the teeth and diagnosis are depicted on a chart using color codes [2]

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Figure 4. Example of Electronic Dental Record:

The diagnosis is depicted on a chart using graphical symbols [3], [4]

An example of dental database, created in premiere in Iasi, was achieved in the WHO Centre from the Faculty of Dental Medicine, with the occasion of a national statistic study regarding the population’s oral health status in correlation with the socio-economic situation, environment factors, anthropological data, the psychological attitude about the oral hygiene and pathology, the general health status and the knowing of the measures of stomatological prophylactics. This study led to a database with 18392 records from the whole country, and supplied us a collection of valuable data for the scientific research in the dentistry field.

The main advantage of this database consisted in the large amount of data collected, recorded on many coordinates, concerning multiple aspects of oral health.

The stomatological diagnosis was also recorded in a very detailed way: on each tooth and, separately, on each tooth’s face. In this way, we recorded in fact the primary diagnosis, with all the details – which allows us to make not only basic statistics interpretations, but also to make new data inferences using mathematic methods.

One demand was to calculate statistics about the Kennedy classification in partial reduced edentulism on the whole dataset and on specific subsets, and we will discuss further aspects concerning the solutions we found at this problem.

2 Problem Formulation It wasn’t very difficult to process this database, because all the information we needed were stored there, in separate fields, in primary form – therefore being easy to make new inferences about them (because, if the information were stored already interpreted or combined in specific ways, in order to make new inferences two steps were necessary: first, to decompose them in basic units, and secondly, to build the new algorithms – and we didn’t have any guarantees that we didn’t lose significant data in the first stage of our work).

So, as we already said, the information concerning the patient’s dental status was recorded for each tooth separately and, even more, for each tooth’s face. Each tooth had allocated a few fields in the general database: one field with the general diagnosis on tooth and another 2 or 5 fields for the diagnosis localization on tooth’s faces. The fields were named using the FDI two-digits notation system: starting from 11 to 18, then 21 to 28 (maxillary teeth – left – right), 31 to 38 and 41 to 48 (mandible teeth – left – right). The diagnosis was recorded using Character fields, according to the following codifications:

0 – healthy tooth; 1.1 – superficial decay;

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1.2 – decay in enamel; 1.3 – decay in dentin, without dental pulp

involvement; 1.4 – decay in dentin, with dental pulp involvement; 1.5 – deep decay; 2 – obstructed and decayed tooth; 3 – obstructed and not-decayed tooth; 4 – tooth absent by decay; 5 – tooth absent by other causes; 6 – sealed tooth; 7 – pillar bridge; 8 – body bridge; 9 – un-erupted tooth; 10 – position anomalies; 11 – morphological anomalies.

In order to calculate statistics about the Kennedy

classification in partial reduced edentulism, we had to record, for each patient in our database, his edentulism type and, after this, his Kennedy classification. These tasks involve two problems: 1. To define for each patient the edentulism type – according to his dental status; 2. To define the Kennedy classification.

The edentulism type [5] is characterized using the following definitions: - Total edentulism: all teeth absent on an arch

(maxilla or mandible) – or 16 teeth absent; - Subtotal edentulism: the presence on an arch of

maximum 3 teeth, without concerning about their position – the number of absent teeth between 13 and 16;

- Partial extensive edentulism: over 3 teeth absent in the lateral area (teeth 4 – 8) or over 4 teeth

absent in the frontal area (teeth 1 – 3) – which means, from a mathematical point of view: o 12 absent teeth, or o < 12 absent teeth and ≥ 4 consecutive absent

teeth in the lateral area. - Partial reduced edentulism: at most 3

consecutive absent teeth in the lateral area (teeth 4 – 8) or at most 4 absent teeth in frontal area – or 12 absent teeth, and 16 – 13 – 23 – 26 teeth present on the maxilla, respectively 36 – 33 – 43 – 46 teeth present on the mandible.

The Kennedy classification [6] is characterized using the following definitions: - Kennedy 1st class: the absence of teeth on the

arch at its both ends; - Kennedy 2nd class: the absence of teeth on the

arch at a single end; - Kennedy 3rd class: the interspersed teeth

absence – the terminal teeth being present; - Kennedy 4th class: frontal edentulism.

3 Problem Solution Starting from these definitions, we built the algorithms that solve our problem. Because the database was designed in FOXPRO, we also chose FOX PRO as programming language [7], [8], [9]. We will present further these algorithms written in pseudo code, based on the previous definitions.

The algorithm that identifies for each patient his edentulism type has the following structure:

EDENTULISM TYPES:

begin

open the database

for each teeth

store occurs (‘4’) to nra

store occurs (‘5’) to nrb

store occurs (‘8) to nrd

end for

calculate number of absent teeth with code 4 on each arch (mx_a4, md_a4)

calculate number of absent teeth with code 5 on each arch (mx_a5, md_a5)

calculate number of absent teeth with code 8 on each arch (mx_a8, md_a8)

calculate number of absent teeth on each arch (mx_a, md_a)

calculate number of lateral absent teeth on each arch {teeth 4 – 8, codes ‘4’, ‘5’ or ‘8’}

(mx_lat_a, md_lat_a)

calculate number of frontal absent teeth on each arch {teeth 1 – 3, codes ‘4’, ‘5’ or ‘8’}

(mx_fr_a, md_fr_a)

{label the ET patients}

write ET_MX for (mx_a + mx_a8) = 16 AND (md_a + md_a8) <> 16

write ET_MD for (md_a + md_a8) = 16 AND (mx_a + mx_a8) <> 16

write ET_XD for (mx_a + mx_a8) = 16 AND (md_a + md_a8) = 16

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{label the ETS patients}

write ETS_MX for (mx_a + mx_a8) >= 13 AND (mx_a + mx_a8) < 16

write ETS_MD for (md_a + md_a8) >= 13 AND (md_a + md_a8) < 16

write ETS_XD for ((mx_a + mx_a8) >= 13 AND (mx_a + mx_a8) < 16) AND ((md_a +

md_a8) >= 13 AND (md_a + md_a8) < 16)

{label the EPI patients}

write EPI_MX for

12 teeth absents and 16 – 13 – 23 – 26 presents or

< 12 teeth absents and 18 – 17 – 16 – 15 teeth absents or

< 12 teeth absents and 18 – 17 – 16 – 15 – 14 teeth absents or

< 12 teeth absents and 18 – 17 – 16 – 15 – 14 – 13 teeth absents or

< 12 teeth absents and 18 – 17 – 16 – 15 – 14 – 13 – 12 teeth absents or

< 12 teeth absents and 18 – 17 – 16 – 15 – 14 – 13 – 12 – 11 teeth absents or

< 12 teeth absents and 18 – 17 – 16 – 15 – 14 – 13 – 12 – 11 – 21 teeth absents or

< 12 teeth absents and 17 – 16 – 15 – 14 teeth absents or

< 12 teeth absents and 17 – 16 – 15 – 14 – 13 teeth absents or

< 12 teeth absents and 17 – 16 – 15 – 14 – 13 – 12 teeth absents or

< 12 teeth absents and 17 – 16 – 15 – 14 – 13 – 12 – 11 teeth absents or

< 12 teeth absents and 17 – 16 – 15 – 14 – 13 – 12 – 11 – 21 teeth absents or

< 12 teeth absents and 16 – 15 – 14 – 13 teeth absents or

< 12 teeth absents and 16 – 15 – 14 – 13 – 12 teeth absents or

< 12 teeth absents and 16 – 15 – 14 – 13 – 12 – 11 teeth absents or

< 12 teeth absents and 16 – 15 – 14 – 13 – 12 – 11 – 21 teeth absents or

< 12 teeth absents and 15 – 14 – 13 – 12 teeth absents or

< 12 teeth absents and 15 – 14 – 13 – 12 – 11 teeth absents or

< 12 teeth absents and 15 – 14 – 13 – 12 – 11 – 21 teeth absents or

repeat the previous 18 lines for right side teeth (28 – 21) or

< 12 teeth absents and 13 – 12 – 11 – 21 – 22 teeth absents or

< 12 teeth absents and 12 – 11 – 21 – 22 – 23 teeth absents or

< 12 teeth absents and 13 – 12 – 11 – 21 – 22 – 23 teeth absents

write EPI_MD for {similar combinations involving 31 – 38, 41 – 48 teeth}

{label the EPR patients}

write EPR_MX for (mx_a + mx_a8)>0 and et=' ' and ets=' ' and epimx=' '

write EPR_MD for (md_a + md_a8)>0 and et=' ' and ets=' ' and epimd=' '

end

The algorithm that identifies for each EPR patient his Kennedy class has the following structure:

KENNEDY CLASSES – EPR:

begin

open the database

select the EPR patients

{label the 1st Kennedy class patients}

write KEN_1_MX for

18 – 28 teeth absents or

18 – 28 – 27 teeth absents or

18 – 28 – 27 – 26 teeth absents or

18 – 17 – 28 teeth absents or

18 – 17 – 28 – 27 teeth absents or

18 – 17 – 28 – 27 – 26 teeth absents or

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18 – 17 – 16 – 28 teeth absents or

18 – 17 – 16 – 28 – 27 teeth absents or

18 – 17 – 16 – 28 – 27 – 26 teeth absents or

{label the 2nd Kennedy class patients}

write KEN_2_MX for

18 absent and 28 present or

18 – 17 absents and 28 present or

18 – 17 – 16 absents and 28 present or

28 absent and 18 present or

28 – 27 absents and 18 present or

28 – 27 – 26 absents and 18 present or

{label the 3rd Kennedy class patients}

write KEN_3_MX for

18 present and 28 present

{label the 4th Kennedy class patients}

write KEN_4_MX for

mx_fr_a > 0 AND mx_fr_a <= 4 AND mx_lat_a = 0

write KEN_1_MD, KEN_2_MD, KEN_3_MD, KEN_4_MD for {similar combinations

involving 31 – 38, 41 – 48 teeth}

end

4 Conclusion These algorithms were very useful and they were practically the unique solution to obtain the desired statistics – because, otherwise, it was necessary to analyze again, by hand, all the 18392 records and to fulfill the edentation type and the Kennedy classification fields. Maybe this operation is possible for small databases – but when we are dealing with very large sets of data, the time involved make such initiatives prohibited.

Anyway, it is not always very easy to find such solutions – especially when the databases are wrong designed [9]. That is way it is very important to pay the necessary attention to the databases designing and, always when it is possible, to record the information decomposed in its basic components – because, otherwise, it is very difficult and sometimes, impossible, to recombine the stored information in order to obtain new inferences.

The problem we have to deal with in this framework is the important time costs – especially when we are dealing with large amounts of records, because, in order to store all the necessary details for an accurate oral diagnosis for each patient we need a lot of time. In order to simplify this step new solutions can be used - for example to scan the written health record of the patient and to recognize automatically the significant information, through a software based on the OCR technology principles – which, of course, must be especially developed in this purpose. Such a solution can be adopted when there are strong enough reasons to do this, regarding its financial efficiency and its future possible

applications – because, only for isolated use, the programming effort may not be justified.

References:

[1] http://www.fdiworldental.org:8080/content/two-digit-notation [2] http://www.choudhurydental.com [3] N. Forna, coord., Actualities in the Clinics and Therapy of Partially Extended Edentulism, “Gr.T.Popa” Publishing House, Iasi, 2009 (in Romanian) [4] N. Forna, coord., Dental Prosthetics, Enciclopedica Publishing House, Bucuresti, 2011 (in Romanian) [5] V. Burlui, N. Forna, The Clinics and Therapy of Partially Extended Edentulism, Apollonia Publishing House, Iasi, 2003 (in Romanian) [6] N. Forna, V. Burlui, The Clinics and Therapy of Edentulism and Maxilla-Facial Substance Loss, Apollonia Publishing House, Iasi, 1998 (in Romanian) [7] G. Dima, M. Dima, FOXPRO, Teora Publishing House, Bucuresti, 1996 (in Romanian) [8] W. Hentzen, The Software Developer’s Guide, Hentzenwerke Publishing, Whitefish Bay, USA, 2002 [9] K. Roebuck, Database Administration: High-impact Strategies: What You Need to Know:

Definitions, Adoptions, Impact, Benefits, Maturity,

Vendors, Tebbo Publishing, 2011

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