Dataset Information

Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset.

ABSTRACT: Preoperative radiological identification of mandibular canals is essential for maxillofacial surgery. This study demonstrates the reproducibility of a deep learning system (DLS) by evaluating its localisation performance on 165 heterogeneous cone beam computed tomography (CBCT) scans from 72 patients in comparison to an experienced radiologist's annotations. We evaluated the performance of the DLS using the symmetric mean curve distance (SMCD), the average symmetric surface distance (ASSD), and the Dice similarity coefficient (DSC). The reproducibility of the SMCD was assessed using the within-subject coefficient of repeatability (RC). Three other experts rated the diagnostic validity twice using a 0-4 Likert scale. The reproducibility of the Likert scoring was assessed using the repeatability measure (RM). The RC of SMCD was 0.969 mm, the median (interquartile range) SMCD and ASSD were 0.643 (0.186) mm and 0.351 (0.135) mm, respectively, and the mean (standard deviation) DSC was 0.548 (0.138). The DLS performance was most affected by postoperative changes. The RM of the Likert scoring was 0.923 for the radiologist and 0.877 for the DLS. The mean (standard deviation) Likert score was 3.94 (0.27) for the radiologist and 3.84 (0.65) for the DLS. The DLS demonstrated proficient qualitative and quantitative reproducibility, temporal generalisability, and clinical validity.

SUBMITTER: Jarnstedt J

PROVIDER: S-EPMC10465591 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset.

Järnstedt Jorma J Sahlsten Jaakko J Jaskari Joel J Kaski Kimmo K Mehtonen Helena H Hietanen Ari A Sundqvist Osku O Varjonen Vesa V Mattila Vesa V Prapayasatok Sangsom S Nalampang Sakarat S

Scientific reports 20230829 1

Preoperative radiological identification of mandibular canals is essential for maxillofacial surgery. This study demonstrates the reproducibility of a deep learning system (DLS) by evaluating its localisation performance on 165 heterogeneous cone beam computed tomography (CBCT) scans from 72 patients in comparison to an experienced radiologist's annotations. We evaluated the performance of the DLS using the symmetric mean curve distance (SMCD), the average symmetric surface distance (ASSD), and ...[more]

PMID: 37644067

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Project description:BackgroundThe study objective was to determine whether unlabeled datasets can be used to further train and improve the accuracy of a deep learning system (DLS) for the detection of tuberculosis (TB) on chest radiographs (CXRs) using a two-stage semi-supervised approach.MethodsA total of 111,622 CXRs from the National Institute of Health ChestX-ray14 database were collected. A cardiothoracic radiologist reviewed a subset of 11,000 CXRs and dichotomously labeled each for the presence or absence of potential TB findings; these interpretations were used to train a deep convolutional neural network (DCNN) to identify CXRs with possible TB (Phase I). The best performing algorithm was then used to label the remaining database consisting of 100,622 radiographs; subsequently, these newly-labeled images were used to train a second DCNN (phase II). The best-performing algorithm from phase II (TBNet) was then tested against CXRs obtained from 3 separate sites (2 from the USA, 1 from China) with clinically confirmed cases of TB. Receiver operating characteristic (ROC) curves were generated with area under the curve (AUC) calculated.ResultsThe phase I algorithm trained using 11,000 expert-labelled radiographs achieved an AUC of 0.88. The phase II algorithm trained on images labeled by the phase I algorithm achieved an AUC of 0.91 testing against a TB dataset obtained from Shenzhen, China and Montgomery County, USA. The algorithm generalized well to radiographs obtained from a tertiary care hospital, achieving an AUC of 0.87; TBNet's sensitivity, specificity, positive predictive value, and negative predictive value were 85%, 76%, 0.64, and 0.9, respectively. When TBNet was used to arbitrate discrepancies between 2 radiologists, the overall sensitivity reached 94% and negative predictive value reached 0.96, demonstrating a synergistic effect between the algorithm's output and radiologists' interpretations.ConclusionsUsing semi-supervised learning, we trained a deep learning algorithm that detected TB at a high accuracy and demonstrated value as a CAD tool by identifying relevant CXR findings, especially in cases that were misinterpreted by radiologists. When dataset labels are noisy or absent, the described methods can significantly reduce the required amount of curated data to build clinically-relevant deep learning models, which will play an important role in the era of precision medicine.

Project description:ObjectivesThis study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this is the first study to evaluate the application of deep learning for dental caries in CBCT images.MethodsThe CBCT image dataset comprised 382 molar teeth with caries and 403 noncarious molar cases. The dataset was divided into a development set for training and validation and test set. Three images were obtained for each case, including axial, sagittal, and coronal. The test dataset was provided to a multiple-input convolutional neural network (CNN). The network made predictions regarding the presence or absence of dental decay and classified the lesions according to their depths and types for the provided samples. Accuracy, sensitivity, specificity, and F1 score values were measured for dental caries detection and classification.ResultsThe diagnostic accuracy, sensitivity, specificity, and F1 score for caries detection in carious molar teeth were 95.3%, 92.1%, 96.3%, and 93.2%, respectively, and for noncarious molar teeth were 94.8%, 94.3%, 95.8%, and 94.6%. The CNN network showed high sensitivity, specificity, and accuracy in classifying caries extensions and locations.ConclusionsThis research demonstrates that deep learning models can accurately identify dental caries and classify their depths and types with high accuracy, sensitivity, and specificity. The successful application of deep learning in this field will undoubtedly assist dental practitioners and patients in improving diagnostic and treatment planning in dentistry.Clinical significanceThis study showed that deep learning can accurately detect and classify dental caries. Deep learning can provide dental caries detection accurately. Considering the shortage of dentists in certain areas, using CNNs can lead to broader geographic coverage in detecting dental caries.

Dataset Information

Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset.

Publications

Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset.

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