User acceptance of a touchless sterile system to control virtual orthodontic study models

Hassan, Wan Zulkifli Wan; Kassim, Noor Lide Abu; Jhawar, Abhishek; Shurkri, Norsyafiqah Mohd; Baharin, Nur Azreen Kamarul; Chan, Chee Seng

doi:10.1016/j.ajodo.2015.10.018

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…Data retention and reuse time must be explicit, while the need to retain the files for defensive dentistry (i.e., medical-legal and insurance reasons) or for future appliance repair is a matter of discussion. Orthodontists can easily backup the digital data and keep them for at least 10 years; meanwhile, gypsum casts could be lost or broken or need space in dental office, in addition to being a hidden source of contamination [106, 107].…”

Section: Resultsmentioning

confidence: 99%

Innovation by Computer-Aided Design/Computer-Aided Manufacturing Technology: A Look at Infection Prevention in Dental Settings

Barenghi¹,

Barenghi

Cadeo

et al. 2019

BioMed Research International

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Recent data indicates limited awareness and compliance on infection prevention procedures by dental offices and by dental laboratories. Guidelines for infection prevention in dentistry have been published by Centres for Disease Control and Prevention since 2003; the section “IX-Special consideration” includes a subsection concerning the prevention in dental laboratories, but it has not been modernised in later versions to fit the needs of traditional and computer-aided technology. Traditional techniques required disinfecting items (impression, chewing waxes, and appliances) with well-suited products, which are also chosen for limiting impression changes or appliance deterioration. Effective procedures are available with difficulties. Some of these contain irritant or non-eco-friendly disinfectants. The transport of impression, to dental laboratories, is often delayed with limited precautions for limiting cross-infection. Gypsum casts are frequently contaminated mainly by bacteria and their antibiotic-resistant strains and even stored for long periods during dental implant supported restoration and orthodontic therapy, becoming a hidden source of infection. Nowadays, computer-aided design/computer-aided manufacturing technology seems to be an interesting way to promote both business and safety, being more comfortable for patients and more accurate than traditional technology. A further advantage is easier infection prevention since, for the most part, mainly digital impression and casts are not a source of cross-infection and the transport of contaminated items is reduced and limited to try-in stages. Nevertheless, a peculiar feature is that a digital electronic file is of course unalterable, but may be ruined by a computer virus. Additionally, the reconditioning of scanner tips is determinant for the optical characteristics and long term use of the scanner, but information for its reconditioning from producers is often limited. This study focuses on some critical points including (a) insufficient guidelines, (b) choice of proper procedure for scanner reconditioning, and (c) data protection in relation to patient privacy.

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Section: Resultsmentioning

confidence: 99%

Innovation by Computer-Aided Design/Computer-Aided Manufacturing Technology: A Look at Infection Prevention in Dental Settings

Barenghi¹,

Barenghi

Cadeo

et al. 2019

BioMed Research International

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“…Akçam and Takada, (2002), (27) Fuzzy modelling for selecting headgear types A fuzzy model that can infer precise choice of headgear types appropriate to the treatment of an orthodontic case.…”

Section: Diagnosis Of Dental Deformities In Cephalometry Images Usingmentioning

confidence: 99%

“…Six of these 26 articles described orthodontic diagnosis and treatment outcomes using AI and super vector regression [21][22][23][24][25][26]. Sterilization of the models was provided by a touchless sterile method in 1 study [27]. Headgear selection was performed by using the fuzzy method, which is one of the basic methods of artificial intelligence, in the study performed by Akçam and Takada [28].…”

Section: Diagnosis Of Dental Deformities In Cephalometry Images Usingmentioning

confidence: 99%

Artificial intelligence and machine learning in orthodontics

Büyük

Hatal

2019

Ortadogu Tıp Derg

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Objective:The clinical use of information technology in orthodontics has increased significantly in recent years. The aim of this systematic review is to perform a scientific analysis of artificial intelligence and machine learning in orthodontics. Methods:An electronic search and manual search were performed on September 25, 2018 about using artificial intelligence and machine learning in orthodontics. Results:A total of 107 studies were found. Nine studies were excluded because of duplication. After exclusion of all the irrelevant and non-English articles, 23 full-text articles remained to be included in this systematic review. 3 additional articles were included in this systematic review. Twelve automatic cephalometric landmark determination, 6 orthodontic diagnosis and treatment outcomes, 2 orthodontic tooth extraction decision, 3 facial attractiveness, 1 headgear selection, 1 touchless sterilisation system and 1 automatic skeletal age determination studies were included in this systematic review. Conclusions:Artificial intelligence and machine learning are mainly focused on determination of automatic cephalometric points, facial attractiveness and tooth extraction decisions for orthodontic purposes. The use of artificial intelligence in orthodontics is important in terms of obtaining more accurate and rapid results clinically.

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“…Healthcare environments are using a gesture-based interaction and visualisation for touch-free NUIs in combination with a Kinect or LMC sensor. This, for example, is used to explore Computed Tomography (CT) volume datasets [ 50 ] or 3D orthodontic models [ 51 ]. Corresponding concepts and solutions also enable use during surgery [ 52 , 53 ].…”

Section: Applications and Contextsmentioning

confidence: 99%

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Bachmann

Weichert

Rinkenauer

2018

Sensors

127

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Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.

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User acceptance of a touchless sterile system to control virtual orthodontic study models

Abstract: This study supports the adoption of this promising development for a sterile touch-free patient record-management system.

Cited by 10 publications

References 19 publications

Innovation by Computer-Aided Design/Computer-Aided Manufacturing Technology: A Look at Infection Prevention in Dental Settings

Innovation by Computer-Aided Design/Computer-Aided Manufacturing Technology: A Look at Infection Prevention in Dental Settings

Artificial intelligence and machine learning in orthodontics

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

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