Abstract：
BACKGROUND: This research aimed to investigate the concordance between clinical impressions and histopathologic diagnoses made by clinicians and artificial intelligence tools for odontogenic keratocyst (OKC) and Odontogenic tumours (OT) in a New Zealand population from 2008 to 2023.
METHODS: Histopathological records from the Oral Pathology Centre, University of Otago (2008-2023) were examined to identify OKCs and OT. Specimen referral details, histopathologic reports, and clinician differential diagnoses, as well as those provided by ORAD and Chat-GPT4, were documented. Data were analyzed using SPSS, and concordance between provisional and histopathologic diagnoses was ascertained.
RESULTS: Of the 34,225 biopsies, 302 and 321 samples were identified as OTs and OKCs. Concordance rates were 43.2% for clinicians, 45.6% for ORAD, and 41.4% for Chat-GPT4. Corresponding Kappa value against histological diagnosis were 0.23, 0.13 and 0.14. Surgeons achieved a higher concordance rate (47.7%) compared to non-surgeons (29.82%). Odds ratio of having concordant diagnosis using Chat-GPT4 and ORAD were between 1.4 and 2.8 (p < 0.05). ROC-AUC and PR-AUC were similar between the groups (Clinician 0.62/0.42, ORAD 0.58/0.28, Char-GPT4 0.63/0.37) for ameloblastoma and for OKC (Clinician 0.64/0.78, ORAD 0.66/0.77, Char-GPT4 0.60/0.71).
CONCLUSIONS: Clinicians with surgical training achieved higher concordance rate when it comes to OT and OKC. Chat-GPT4 and Bayesian approach (ORAD) have shown potential in enhancing diagnostic capabilities.
METHODS: Histopathological records from the Oral Pathology Centre, University of Otago (2008-2023) were examined to identify OKCs and OT. Specimen referral details, histopathologic reports, and clinician differential diagnoses, as well as those provided by ORAD and Chat-GPT4, were documented. Data were analyzed using SPSS, and concordance between provisional and histopathologic diagnoses was ascertained.
RESULTS: Of the 34,225 biopsies, 302 and 321 samples were identified as OTs and OKCs. Concordance rates were 43.2% for clinicians, 45.6% for ORAD, and 41.4% for Chat-GPT4. Corresponding Kappa value against histological diagnosis were 0.23, 0.13 and 0.14. Surgeons achieved a higher concordance rate (47.7%) compared to non-surgeons (29.82%). Odds ratio of having concordant diagnosis using Chat-GPT4 and ORAD were between 1.4 and 2.8 (p < 0.05). ROC-AUC and PR-AUC were similar between the groups (Clinician 0.62/0.42, ORAD 0.58/0.28, Char-GPT4 0.63/0.37) for ameloblastoma and for OKC (Clinician 0.64/0.78, ORAD 0.66/0.77, Char-GPT4 0.60/0.71).
CONCLUSIONS: Clinicians with surgical training achieved higher concordance rate when it comes to OT and OKC. Chat-GPT4 and Bayesian approach (ORAD) have shown potential in enhancing diagnostic capabilities.
摘要：
背景：这项研究旨在调查2008年至2023年新西兰人群牙源性角化囊肿（OKC）和牙源性肿瘤（OT）的临床医生和人工智能工具的临床印象与组织病理学诊断之间的一致性。
方法：口腔病理学中心的组织病理学记录，奥塔哥大学(2008-2023年)进行了检查，以确定OKC和OT。标本转介详情,组织病理学报告，和临床医生的鉴别诊断，以及ORAD和Chat-GPT4提供的文件都有记录。使用SPSS对数据进行分析，并确定了临时诊断和组织病理学诊断之间的一致性。
结果：在34,225例活检中，302和321个样品被鉴定为OTs和OKC。临床医生的一致率为43.2%，ORAD的45.6%，Chat-GPT4为41.4%。与组织学诊断相对应的Kappa值分别为0.23、0.13和0.14。与非外科医生（29.82％）相比，外科医生的一致率（47.7％）更高。使用Chat-GPT4和ORAD进行一致诊断的几率在1.4和2.8之间(p<0.05)。成釉细胞瘤和OKC组之间的ROC-AUC和PR-AUC相似（临床医生0.62/0.42，ORAD0.58/0.28，Char-GPT40.63/0.37）（临床医生0.64/0.78，ORAD0.66/0.77，Char-GPT40.60/0.71）。
结论：接受手术训练的临床医生在OT和OKC方面取得了更高的一致率。Chat-GPT4和贝叶斯方法（ORAD）已显示出增强诊断能力的潜力。
方法：口腔病理学中心的组织病理学记录，奥塔哥大学(2008-2023年)进行了检查，以确定OKC和OT。标本转介详情,组织病理学报告，和临床医生的鉴别诊断，以及ORAD和Chat-GPT4提供的文件都有记录。使用SPSS对数据进行分析，并确定了临时诊断和组织病理学诊断之间的一致性。
结果：在34,225例活检中，302和321个样品被鉴定为OTs和OKC。临床医生的一致率为43.2%，ORAD的45.6%，Chat-GPT4为41.4%。与组织学诊断相对应的Kappa值分别为0.23、0.13和0.14。与非外科医生（29.82％）相比，外科医生的一致率（47.7％）更高。使用Chat-GPT4和ORAD进行一致诊断的几率在1.4和2.8之间(p<0.05)。成釉细胞瘤和OKC组之间的ROC-AUC和PR-AUC相似（临床医生0.62/0.42，ORAD0.58/0.28，Char-GPT40.63/0.37）（临床医生0.64/0.78，ORAD0.66/0.77，Char-GPT40.60/0.71）。
结论：接受手术训练的临床医生在OT和OKC方面取得了更高的一致率。Chat-GPT4和贝叶斯方法（ORAD）已显示出增强诊断能力的潜力。
