Customized convolutional neural network to detect dental caries from radio visio graphy(RVG) images

Abstract

Dental caries, also known as tooth decay, is one of the extensive chronic diseases worldwide caused by the breakdown of tooth enamel [1]. Tooth decay causing bacteria in the mouth make acid that attacks tooth enamel, leading to a small hole in the tooth. Dental caries develops as a result of multiple interactions between acid-producing bacteria and fermentable carbohydrates, teeth, and saliva [2]. The mouth's correct and efficient care affects a person's general health and beauty. Oral diseases have to be treated on time or else they lead to gum problems, dental caries, tooth loss, bone loss, and other dental issues [3]. Furthermore, oral disorders affect over 3.5 billion people worldwide; dental caries is the most common disease, with a rising prevalence in many low-and middle-income countries [4].

Deep Learning to Detect and Classify Teeth, Dental Caries and Restorations: A Systematic Mapping

“…(to be continued on next page) (533/59/-) PAN 3 dentists (+10y) yes [77] (1250/-/125) PERI dentists (unspecified) yes [81] (3293/252/141) BTW 4 dentists (+3y) yes [82] (492/65/64) BTW 1 dentist + 1 radiologist (+9y) yes [15] (800/-/200) BTW 2 dentists (+10y) yes [83] photos + 120 x-rays * [57] Photos + X-Rays 2 dentists (+3y) yes [1] (1071/-/89) PAN 4 dentists (3-15y) yes [88] BTW 1 experienced dentist in oral radiology yes [38] PAN radiologists (unspecified) yes [34] (30/11/10) PAN 1 dentist + 1 radiologist yes [40] (447/127/61) PAN -yes [37] (485/69/139) PERI 2 dentists + 1 resident dentist yes [61] (80/-/20) PAN 2 dentists yes [84] (400/50/50) BTW 1 radiologist (11y) and a research assistant (3y) yes [62] (1200/150/150) PAN Clinicians (unspecified) yes [85] (935/117/117) PERI 1 radiologist (12y) and a research assistant (2y) yes [51] (76/-/32) PAN 3 dentists (+3y) yes [52] (980/-/420) PAN 3 dentists (+3y) yes [63] (90/-/10) PAN Radiologists (unspecified, +5y) yes [53] (1104/111/121) RVG Dentists (unspecified) yes [89] (175/-/75) PAN Dentists (unspecified, +5y) yes [87] (457/-/195) Unspecified 4 dentists yes [78] (193/83/1224) PAN -no [66] (2507/835/835) PAN 1 dentist + 1 dental student (last year) yes [79] PANs + 682 PERI/BTW * [3] PAN + PERI + BTW 3 dentists yes [86] PAN dentists (unspecified) yes [35] (1005/335/335) BTW 2 dentists yes [80] (1000/0/200) BTW Dentists (unspecified) no [41] ( (iii) number and expertise of professionals involved in the annotation task and (iv) whether the paper presents any information about the dataset annotation protocol. This information is used to investigate RQ1 Concerning RQ1.1, the selected papers exploited three different types of radiograph: panoramic (forty-six papers, 66.67%), periapical (nine papers, 13.04%), and bitewing (eight papers, 11.59%).…”

“…This information is used to investigate RQ1 Concerning RQ1.1, the selected papers exploited three different types of radiograph: panoramic (forty-six papers, 66.67%), periapical (nine papers, 13.04%), and bitewing (eight papers, 11.59%). One study (1.45%) combined panoramic and periapical [45], another combined periapical and bitewing [74], another combined panomaric, periapical and bitewing [79], another used RadioVisioGraphy (RVG) [53] and another manipulated panoramic and high-definition photographs of the mouth [83]. One article did not specify the type of radiograph [87].…”