This application claims priority to Taiwan Application Serial Number 109101588, filed Jan. 16, 2020, which is herein incorporated by reference.
The present disclosure relates to a repairing method and a repairing system. More particularly, the present disclosure relates to a method of repairing an oral defect model and a system of repairing the oral defect model.
Teeth of the oral often cause to conditions of dentine abrasivity or missing tooth by serious cavities, injury, periodontal diseases or congenital missing tooth. Generally, dentists make the fixed dental crown, dental bridge or removable artificial teeth according to the condition of the missing tooth of the patient to recovery the demand of the chewing, pronunciation and appearance. If the abutment teeth of the patient can meet the requirement, the fixed artificial crown may be first priority.
The conventional techniques can obtain a model of artificial abutment teeth by a conventional impression or a digital intraoral scanner. The model is poured after conventional impression, followed by a step of ditching is performed to complete a die. There might be defects in both conventional and digital impression method caused by saliva, blood, air bubbles, etc. causing incontinuous finish line or prepared abutment surfaces. The technicians modified the model by their self-experience with a magnifier.
Thus, a method and a system of repairing the oral defect model utilize the automation and artificial intelligence algorithm instead of conventional human experience to reduce the deviation caused by human are commercially desirable.
According to one aspect of the present disclosure, a method of repairing an oral defect model is performed. The method of repairing the oral defect model includes performing a three-dimensional oral defect model obtaining step, a defect cutting line detecting step, a defect point selecting step and a smoothing step. The three-dimensional oral defect model obtaining step is performed to drive a scanner to scan an oral cavity to obtain a three-dimensional oral defect model message. The defect cutting line detecting step is performed to drive a processor to detect a defect cutting line of the three-dimension oral defect model message, and drive a displayer to display the defect cutting line. The defect cutting line includes at least one defect feature point. The defect point selecting step is performed to select the at least one defect feature point of the defect cutting line via the displayer. The smoothing step is performed to drive the processor to perform a smoothing process at the at least one defect feature point, and convert the three-dimensional oral defect model message into a three-dimensional oral repaired model message to smooth the defect cutting line.
According to another aspect of the present disclosure, a method of repairing an oral defect model is performed. The method of repairing the oral defect model includes performing a three-dimensional oral defect model obtaining step, a defect cutting line detecting step, a defect point selecting step and a smoothing step. The three-dimensional oral defect model obtaining step is performed to drive a scanner to scan an oral cavity model to obtain a three-dimensional oral defect model message. The defect cutting line detecting step is performed to drive a processor to detect a defect cutting line of the three-dimension oral defect model message, and drive a displayer to display the defect cutting line. The defect cutting line includes at least one defect feature point. The defect point selecting step is performed to select the at least one defect feature point of the defect cutting line via the displayer. The smoothing step is performed to drive the processor to perform a smoothing process at the at least one defect feature point, and convert the three-dimensional oral defect model message into a three-dimensional oral repaired model message to smooth the defect cutting line.
According to further another aspect of the present disclosure, a system of repairing an oral defect model includes a scanner, a processor and a displayer. The scanner is configured to scan one of an oral cavity and an oral cavity model to obtain a three-dimensional oral defect model message. The processor is signally connected to the scanner, the processor receives the three-dimensional oral defect model message and includes a defect cutting line detecting module, a defect point selecting module and a smoothing module. The defect cutting line detecting module detects a defect cutting line of the three-dimensional oral defect model message, and the defect cutting line includes at least one defect feature point. The defect point selecting module is signally connected to the defect cutting line detecting module. The defect point selecting module selects the at least one defect feature point of the defect cutting line. The smoothing module is signally connected to the defect point selecting module. The smoothing module performs a smoothing process at the at least one defect feature point, and converts the three-dimensional oral defect model message into a three-dimensional oral repaired model message to smooth the defect cutting line. The displayer is signally connected to the processor, and the displayer displays the three-dimensional oral defect model message, the defect cutting line and the three-dimensional oral repaired model message.
The present disclosure can be more fully understood by reading the following detailed description of the embodiment, with reference made to the accompanying drawings as follows:
The embodiment will be described with the drawings. For clarity, some practical details will be described below. However, it should be noted that the present disclosure should not be limited by the practical details, that is, in some embodiment, the practical details is unnecessary. In addition, for simplifying the drawings, some conventional structures and elements will be simply illustrated, and repeated elements may be represented by the same labels.
It will be understood that when an element (or device) is referred to as be “connected to” another element, it can be directly connected to the other element, or it can be indirectly connected to the other element, that is, intervening elements may be present. In contrast, when an element is referred to as be “directly connected to” another element, there are no intervening elements present. In addition, the terms first, second, third, etc. are used herein to describe various elements or components, these elements or components should not be limited by these terms. Consequently, a first element or component discussed below could be termed a second element or component.
Please refer to
The three-dimensional oral defect model obtaining step S12 is performed to drive a scanner to scan an oral cavity to obtain a three-dimensional oral defect model message.
The defect cutting line detecting step S14 is performed to drive a processor to detect a defect cutting line 160 of the three-dimension oral defect model message, and drive a displayer to display the defect cutting line 160. The defect cutting line 160 includes at least one defect feature point. In detail, the defect cutting line detecting step S14 includes the sight selecting step S141, the projecting step S142, the interpolating step S143, the controlling point detecting step S144, a line smoothing step S145, a feature cutting line generating step S146, the surface smoothing step S147 and the cutting step S148.
The sight selecting step S141 is performed to select an abutment region 110 of the three-dimensional oral defect model message, and observe the abutment region 110 from a top view of the abutment region 110, as shown in
The projecting step S142 is performed to project the three-dimensional oral defect model message in the abutment region 110 to a two-dimensional plane according to the top view of the abutment region 110 so as to form a two-dimensional projecting point set 120, as shown in
The interpolating step S143 is performed to perform an orthogonal grid interpolation method at the two-dimensional projecting point set 120 to obtain a two-dimensional concentrated projecting point set 130 as shown in
The controlling point detecting step S144 is performed to search a feature region 140 with an annular shape in an equal angle (for example, in 5 degrees) and a radially way out from a departure point, and then search a plurality of triangular grids of the feature region 140. The triangular grids are viewed as a plurality of controlling points 150, as shown in
The line smoothing step S145 is performed to perform a weighted moving average method at the controlling points 150 to smooth the feature region 140. The feature region 140 and the controlling points 150 are positioned on the two-dimensional plane, and the weighted moving average method is the prior art and will not be described herein again.
The feature cutting line generating step S146 is performed to generate a feature cutting lie from the controlling points 150 smoothed by the line smoothing step S145.
The surface smoothing step S147 is performed to perform the weighted moving average method at the feature cutting line to generate the defect cutting line 160, and the defect cutting line 160 is in a closed loop shape, as shown in
The cutting step S148 is performed to cut the defect cutting line 160 to obtain a three-dimensional abutment model 170, as shown in
The defect point selecting step S16 is performed to select the at least one defect feature point of the defect cutting line 160 via the displayer, as shown in
The smoothing step S18 is performed to drive the processor to perform a smoothing process at the at least one defect feature point, and convert the three-dimensional oral defect model message into a three-dimensional oral repaired model message to smooth the defect cutting line 160 in
Please refer to
The three-dimensional oral defect model obtaining step S21 is performed to drive a scanner to scan an oral cavity model to obtain a three-dimensional oral defect model message. The oral cavity model can be a plaster tooth model after an impression, or a model or a scanning generated by artificial intelligence. The three-dimensional oral standard model obtaining step S22 is performed to obtain a three-dimensional oral standard model message from a database, and the three-dimensional oral standard model message is corresponding to the oral cavity. A defect difference exists between the three-dimensional oral defect model message and the three-dimensional oral standard model message. The three-dimensional oral standard model message can be a plaster tooth model ditched by a technician with full experience.
The defect cutting line detecting step S23, the defect point selecting step S24 and the smoothing step S25 are the same as the defect cutting line detecting step S14, the defect point selecting step S16 and the smoothing step S18 in
The deep learning step S26 is performed to drive the processor to perform a deep learning algorithm at the three-dimensional oral repaired model message and the three-dimensional oral standard model message to train the smoothing process and reduce the smoothing difference. The deep learning algorithm is the prior art, and will not be described again. Thus, the method 100a of repairing the oral defect model of the present disclosure can let the defect difference greater than the smoothing difference, in other words, the three-dimensional oral repaired model message generated by the present disclosure can approach the three-dimensional oral standard model message. The method 100a utilizes the automation and artificial intelligence algorithm instead of conventional human experience to reduce the uncomfortable feeling and pressure formed by a dentist obtaining an ideal model, and reduce the deviation caused by human so as to manufacture tight artificial teeth and increase the medical quality.
Please refer to
The scanner 210 is configured to scan one of an oral cavity and an oral cavity model to obtain a three-dimensional oral defect model message. The processor 220 is signally connected to the scanner 210. The processor 220 receives the three-dimensional oral defect model message and includes a defect cutting line detecting module 222, a defect point selecting module 224 and a smoothing module 226. The defect cutting line detecting module 222 can perform the defect cutting line detecting steps S14, S23. Moreover, the defect point selecting module 224 is signally connected to the defect cutting line detecting module 222, and the defect point selecting module 224 can perform the defect point selecting steps S16, S24. Furthermore, the smoothing module 226 is signally connected to the defect point selecting module 224, and the smoothing module 226 performs a smoothing process at the at least one defect feature point, and converts the three-dimensional oral defect model message into a three-dimensional oral repaired model message to smooth the defect cutting line 160. The smoothing module 226 can perform the smoothing steps S18, S25. The displayer 230 is signally connected to the processor 220. The displayer 230 display the three-dimensional oral defect model message, the defect cutting line 160 and the three-dimensional oral repaired model message. Thus, the system 200 of repairing the oral defect model utilizes the smoothing process to repair the defect cutting line 160 of the defective region into a smooth edge line so as to manufacture tight artificial teeth and increase the medical quality.
According to the aforementioned embodiments and examples, the advantages of the present disclosure are described as follows.
1. The method of repairing the oral defect model of the present disclosure utilizes the smoothing process to repair the defect cutting line of the defective region into a smooth edge line, so that the uncomfortable feeling and pressure formed by a dentist obtaining an ideal model can be reduced, and deviation caused by human can also be reduced so as to manufacture tight artificial teeth and increase the medical quality.
2. The method utilizes the automation and artificial intelligence algorithm instead of conventional human experience to reduce the uncomfortable feeling and pressure formed by a dentist obtaining an ideal model, and reduces the deviation caused by human so as to manufacture tight artificial teeth and increase the medical quality.
3. The system of repairing the oral defect model utilizes the smoothing process to repair the defect cutting line of the defective region into a smooth edge line so as to manufacture tight artificial teeth and increase the medical quality.
Although the present disclosure has been described in considerable detail with reference to certain embodiments thereof, other embodiments are possible. Therefore, the spirit and scope of the appended claims should not be limited to the description of the embodiments contained herein.
It will be apparent to those skilled in the art that various modifications and variations can be made to the structure of the present disclosure without departing from the scope or spirit of the disclosure. In view of the foregoing, it is intended that the present disclosure cover modifications and variations of this disclosure provided they fall within the scope of the following claims.
Number | Date | Country | Kind |
---|---|---|---|
109101588 | Jan 2020 | TW | national |
Number | Name | Date | Kind |
---|---|---|---|
6227850 | Chishti | May 2001 | B1 |
6386878 | Pavlovskaia | May 2002 | B1 |
6463344 | Pavloskaia | Oct 2002 | B1 |
10631956 | Raslambekov | Apr 2020 | B1 |
11026767 | Raslambekov | Jun 2021 | B1 |
11191618 | Raslambekov | Dec 2021 | B1 |
11351011 | Raslambekov | Jun 2022 | B1 |
11478336 | Shao | Oct 2022 | B1 |
11553969 | Lang | Jan 2023 | B1 |
20020015934 | Rubbert | Feb 2002 | A1 |
20020055081 | Hughes | May 2002 | A1 |
20020055800 | Nikolskiy | May 2002 | A1 |
20020177108 | Pavlovskaia | Nov 2002 | A1 |
20030139834 | Nikolskiy | Jul 2003 | A1 |
20030231805 | Hultgren | Dec 2003 | A1 |
20040023188 | Pavlovskaia | Feb 2004 | A1 |
20050043837 | Rubbert | Feb 2005 | A1 |
20060090361 | Matsuda | May 2006 | A1 |
20060263739 | Sporbert | Nov 2006 | A1 |
20080154419 | Cheng | Jun 2008 | A1 |
20080261165 | Steingart | Oct 2008 | A1 |
20090068617 | Lauren | Mar 2009 | A1 |
20090098502 | Andreiko | Apr 2009 | A1 |
20090148809 | Kuo | Jun 2009 | A1 |
20090162813 | Glor | Jun 2009 | A1 |
20090246726 | Chelnokov | Oct 2009 | A1 |
20100281370 | Rohaly | Nov 2010 | A1 |
20110196524 | Giasson | Aug 2011 | A1 |
20110276159 | Chun | Nov 2011 | A1 |
20130060532 | Clausen | Mar 2013 | A1 |
20130179126 | Meier | Jul 2013 | A1 |
20130209965 | Fisker | Aug 2013 | A1 |
20130218530 | Deichmann | Aug 2013 | A1 |
20140071126 | Barneoud | Mar 2014 | A1 |
20140142902 | Chelnokov | May 2014 | A1 |
20140278279 | Azernikov | Sep 2014 | A1 |
20140316750 | Jung | Oct 2014 | A1 |
20150238289 | Wouters | Aug 2015 | A1 |
20150238290 | Wouters | Aug 2015 | A1 |
20150245890 | Wouters | Sep 2015 | A1 |
20160012182 | Golay | Jan 2016 | A1 |
20160224690 | Lee | Aug 2016 | A1 |
20160302896 | Miller | Oct 2016 | A1 |
20160374785 | Fridzon | Dec 2016 | A1 |
20170100214 | Wen | Apr 2017 | A1 |
20170273763 | Fisker | Sep 2017 | A1 |
20170312058 | Fisker | Nov 2017 | A1 |
20180085203 | Ramirez | Mar 2018 | A1 |
20180116762 | Kopelman | May 2018 | A1 |
20180263726 | Fares | Sep 2018 | A1 |
20190043255 | Somasundaram | Feb 2019 | A1 |
20190066537 | Van Den Braber | Feb 2019 | A1 |
20190102880 | Parpara | Apr 2019 | A1 |
20190148005 | Domracheva | May 2019 | A1 |
20190164353 | Yancey | May 2019 | A1 |
20190197691 | Chen | Jun 2019 | A1 |
20190231492 | Sabina | Aug 2019 | A1 |
20190337199 | Jo | Nov 2019 | A1 |
20190378344 | Long | Dec 2019 | A1 |
20200005550 | Schneider | Jan 2020 | A1 |
20200008911 | Savic | Jan 2020 | A1 |
20200022790 | Fisker | Jan 2020 | A1 |
20200125070 | Krauser | Apr 2020 | A1 |
20200138551 | Strong | May 2020 | A1 |
20200197138 | Parkar | Jun 2020 | A1 |
20200281689 | Yancey | Sep 2020 | A1 |
20200315744 | Cramer | Oct 2020 | A1 |
20200315754 | Ciriello | Oct 2020 | A1 |
20200320685 | Anssari Moin | Oct 2020 | A1 |
20200349698 | Minchenkov | Nov 2020 | A1 |
20200352678 | Yuan | Nov 2020 | A1 |
20210059796 | Weiss | Mar 2021 | A1 |
20210085238 | Schnabel | Mar 2021 | A1 |
20210100642 | Weiss | Apr 2021 | A1 |
20210106403 | Aptekarev | Apr 2021 | A1 |
20210106410 | Kim | Apr 2021 | A1 |
20210118132 | Kearney | Apr 2021 | A1 |
20210169318 | Sorimoto | Jun 2021 | A1 |
20210196430 | Wilson | Jul 2021 | A1 |
20210200188 | Shah | Jul 2021 | A1 |
20210217233 | Feng | Jul 2021 | A1 |
20210244518 | Ryu | Aug 2021 | A1 |
20210272377 | Nikolskiy | Sep 2021 | A1 |
20210304874 | Nikolskiy | Sep 2021 | A1 |
20210353386 | Raby | Nov 2021 | A1 |
20210353394 | Lee | Nov 2021 | A1 |
20210357688 | Kearney | Nov 2021 | A1 |
20210365736 | Kearney | Nov 2021 | A1 |
20220000592 | Ramirez | Jan 2022 | A1 |
20220008175 | Öjelund | Jan 2022 | A1 |
20220031433 | Diez | Feb 2022 | A1 |
20220036653 | De Somere | Feb 2022 | A1 |
20220047358 | Domroese | Feb 2022 | A1 |
20220079714 | Paraketsov | Mar 2022 | A1 |
20220087791 | Choi | Mar 2022 | A1 |
20220117480 | Kaji | Apr 2022 | A1 |
20220139044 | Koza | May 2022 | A1 |
20220160476 | Kim | May 2022 | A1 |
20220166955 | Gronau | May 2022 | A1 |
20220172430 | Träff | Jun 2022 | A1 |
20220180012 | Chiosa | Jun 2022 | A1 |
20220183771 | Cho | Jun 2022 | A1 |
20220183789 | Ciriello | Jun 2022 | A1 |
20220192786 | Chelnokov | Jun 2022 | A1 |
20220207737 | Parpara | Jun 2022 | A1 |
20220222910 | Salah | Jul 2022 | A1 |
20220246270 | Alvarez | Aug 2022 | A1 |
20220262007 | Cramer | Aug 2022 | A1 |
20220304782 | Derzapf | Sep 2022 | A1 |
20220313402 | Katzman | Oct 2022 | A1 |
20220331072 | Song | Oct 2022 | A1 |
20220338966 | Lancelle | Oct 2022 | A1 |
Number | Date | Country |
---|---|---|
105761252 | Mar 2017 | CN |
105046750 | Aug 2017 | CN |
2011097947 | Aug 2011 | WO |
Number | Date | Country | |
---|---|---|---|
20210220094 A1 | Jul 2021 | US |