Chihiro Tanikawa1,2,3, Chonho Lee4, Jaeyoen Lim1, Ayaka Oka1, Takashi Yamashiro1. 1. Graduate School of Dentistry, Osaka University, Suita, Japan. 2. Center for Advanced Medical Engineering and Informatics, Osaka University, Suita, Japan. 3. Institute for Datability Science, Osaka University, Suita, Japan. 4. Cybermedia Center, Osaka University, Suita, Japan.
Abstract
OBJECTIVES: To determine whether AI systems that recognize cephalometric landmarks can apply to various patient groups and to examine the patient-related factors associated with identification errors. SETTING AND SAMPLE POPULATION: The present retrospective cohort study analysed digital lateral cephalograms obtained from 1785 Japanese orthodontic patients. Patients were categorized into eight subgroups according to dental age, cleft lip and/or palate, orthodontic appliance use and overjet. MATERIALS AND METHODS: An AI system that automatically recognizes anatomic landmarks on lateral cephalograms was used. Thirty cephalograms in each subgroup were randomly selected and used to test the system's performance. The remaining cephalograms were used for system learning. The success rates in landmark recognition were evaluated using confidence ellipses with α = 0.99 for each landmark. The selection of test samples, learning of the system and evaluation of the system were repeated five times for each subgroup. The mean success rate and identification error were calculated. Factors associated with identification errors were examined using a multiple linear regression model. RESULTS: The success rate and error varied among subgroups, ranging from 85% to 91% and 1.32 mm to 1.50 mm, respectively. Cleft lip and/or palate was found to be a factor associated with greater identification errors, whereas dental age, orthodontic appliances and overjet were not significant factors (all, P < .05). CONCLUSION: Artificial intelligence systems that recognize cephalometric landmarks could be applied to various patient groups. Patient-oriented errors were found in patients with cleft lip and/or palate.
OBJECTIVES: To determine whether AI systems that recognize cephalometric landmarks can apply to various patient groups and to examine the patient-related factors associated with identification errors. SETTING AND SAMPLE POPULATION: The present retrospective cohort study analysed digital lateral cephalograms obtained from 1785 Japanese orthodontic patients. Patients were categorized into eight subgroups according to dental age, cleft lip and/or palate, orthodontic appliance use and overjet. MATERIALS AND METHODS: An AI system that automatically recognizes anatomic landmarks on lateral cephalograms was used. Thirty cephalograms in each subgroup were randomly selected and used to test the system's performance. The remaining cephalograms were used for system learning. The success rates in landmark recognition were evaluated using confidence ellipses with α = 0.99 for each landmark. The selection of test samples, learning of the system and evaluation of the system were repeated five times for each subgroup. The mean success rate and identification error were calculated. Factors associated with identification errors were examined using a multiple linear regression model. RESULTS: The success rate and error varied among subgroups, ranging from 85% to 91% and 1.32 mm to 1.50 mm, respectively. Cleft lip and/or palate was found to be a factor associated with greater identification errors, whereas dental age, orthodontic appliances and overjet were not significant factors (all, P < .05). CONCLUSION: Artificial intelligence systems that recognize cephalometric landmarks could be applied to various patient groups. Patient-oriented errors were found in patients with cleft lip and/or palate.
Authors: Andrej Thurzo; Wanda Urbanová; Bohuslav Novák; Ladislav Czako; Tomáš Siebert; Peter Stano; Simona Mareková; Georgia Fountoulaki; Helena Kosnáčová; Ivan Varga Journal: Healthcare (Basel) Date: 2022-07-08
Authors: Mohamed Zahoor Ul Huqh; Johari Yap Abdullah; Ling Shing Wong; Nafij Bin Jamayet; Mohammad Khursheed Alam; Qazi Farah Rashid; Adam Husein; Wan Muhamad Amir W Ahmad; Sumaiya Zabin Eusufzai; Somasundaram Prasadh; Vetriselvan Subramaniyan; Neeraj Kumar Fuloria; Shivkanya Fuloria; Mahendran Sekar; Siddharthan Selvaraj Journal: Int J Environ Res Public Health Date: 2022-08-31 Impact factor: 4.614