Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

Sukegawa, Shintaro; Yoshii, Kazumasa; Hara, Takeshi; Matsuyama, Tamamo; Yamashita, Katsusuke; Nakano, Keisuke; Takabatake, Kiyofumi; Kawai, Hiroki; Nagatsuka, Hitoshi; Furuki, Yoshihiko

doi:10.3390/biom11060815

Cited by 54 publications

(49 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In orthodontics, they can facilitate the diagnosis and treatment planning, cephalometric points marking, anatomic analyses, assessment of growth and development, and the evaluation of treatment outcomes [ 33 , 34 , 36 , 39 , 40 ]. In dental surgery, neural networks may be helpful in orthognathic surgery planning, prediction of post-extraction complications, bone lesion detection, and differentiation and implantological treatment planning [ 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. Furthermore, artificial intelligence is spreading into periodontology and in the above-mentioned studies, it was used to evaluate the periodontal bone loss, peri-implant bone loss, and to predict the development of periodontitis due to the psychological features [ 49 , 50 , 51 , 52 , 53 , 54 ].…”

Section: Discussionmentioning

confidence: 99%

“…Dental implant treatment planning with the usage of three-dimensional cone-beam computed tomography (CBCT) images can be facilitate by AI systems [ 46 ]. Moreover, convolutional neural networks can be used to identify dental implant brands on panoramic radiographs and to identify the stage of treatment [ 47 ]. The quality of the osteointegration can be assessed by using convolutional neural networks ( Table 4 ).…”

Section: Neural Network In Dental Surgerymentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Dentistry—Narrative Review

Ossowska

Kusiak

Świetlik

2022

IJERPH

View full text Add to dashboard Cite

Nowadays, artificial intelligence (AI) is becoming more important in medicine and in dentistry. It can be helpful in many fields where the human may be assisted and helped by new technologies. Neural networks are a part of artificial intelligence, and are similar to the human brain in their work and can solve given problems and make fast decisions. This review shows that artificial intelligence and the use of neural networks has developed very rapidly in recent years, and it may be an ordinary tool in modern dentistry in the near future. The advantages of this process are better efficiency, accuracy, and time saving during the diagnosis and treatment planning. More research and improvements are needed in the use of neural networks in dentistry to put them into daily practice and to facilitate the work of the dentist.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Neural Network In Dental Surgerymentioning

confidence: 99%

Artificial Intelligence in Dentistry—Narrative Review

Ossowska

Kusiak

Świetlik

2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…There have been many studies on the classification of dental implants using CNNs [ 10 – 14 , 16 , 25 ] and these studies have achieved high classification accuracy. However, it is difficult to simply compare classification performance with previous studies because it was analyzed under different conditions such as the number of training and test images, the type of implant, and the model.…”

Section: Discussionmentioning

confidence: 99%

“…However, increasing the number of parameters in the deep layers leads to the burden of calculation cost. By adding another structure to the CNN structure [ 15 ] or changing the structure of the CNNs [ 16 ], various developments have been made, such as achieving the same accuracy with a small number of parameters.…”

Section: Introductionmentioning

confidence: 99%

Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning?

et al. 2022

Self Cite

View full text Add to dashboard Cite

Attention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention branch network (ABN) for implant classification using convolutional neural networks (CNNs). The data consisted of 10191 dental implant images from 13 implant brands that cropped the site, including dental implants as pretreatment, from digital panoramic radiographs of patients who underwent surgery at Kagawa Prefectural Central Hospital between 2005 and 2021. ResNet 18, 50, and 152 were evaluated as CNN models that were compared with and without the ABN. We used accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve as performance metrics. We also performed statistical and effect size evaluations of the 30-time performance metrics of the simple CNNs and the ABN model. ResNet18 with ABN significantly improved the dental implant classification performance for all the performance metrics. Effect sizes were equivalent to “Huge” for all performance metrics. In contrast, the classification performance of ResNet50 and 152 deteriorated by adding the attention mechanism. ResNet18 showed considerably high compatibility with the ABN model in dental implant classification (AUC = 0.9993) despite the small number of parameters. The limitation of this study is that only ResNet was verified as a CNN; further studies are required for other CNN models.

show abstract

“…In this study, the effect size in AUC for osteoporosis identification was 0.871, which was classified as a large effect. Since few reports have calculated the sizes of such effects based on comparisons between DL models [ 28 ], we are confident that our study will play a role as a basic research that helps determine sample sizes for studies in the future.…”

Section: Discussionmentioning

confidence: 99%

Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This study aimed to statistically evaluate the osteoporosis identification ability of deep learning by combining hip radiographs with patient variables. Materials andMethods: We collected a dataset containing 1699 images from patients who underwent skeletal-bone-mineral density measurements and hip radiography at a general hospital from 2014 to 2021. Osteoporosis was assessed from hip radiographs using convolutional neural network (CNN) models (ResNet18, 34, 50, 101, and 152). We also investigated ensemble models with patient clinical variables added to each CNN. Accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC) were calculated as performance metrics. Furthermore, we statistically compared the accuracy of the image-only model with that of an ensemble model that included images plus patient factors, including effect size for each performance metric. Results: All metrics were improved in the ResNet34 ensemble model compared with the image-only model. The AUC score in the ensemble model was significantly improved compared with the image-only model (difference 0.004; 95% CI 0.002–0.0007; p = 0.0004, effect size: 0.871). Conclusions: This study revealed the additional effect of patient variables in identification of osteoporosis using deep CNNs with hip radiographs. Our results provided evidence that the patient variables had additive synergistic effects on the image in osteoporosis identification.

show abstract

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

Cited by 54 publications

References 27 publications

Artificial Intelligence in Dentistry—Narrative Review

Artificial Intelligence in Dentistry—Narrative Review

Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning?

Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis

Contact Info

Product

Resources

About