Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images.

The objective of this study is to use a deep-learning model based on CNN architecture to detect the second mesiobuccal (MB2) canals, which are seen as a variation in maxillary molars root canals. In the current study, 922 axial sections from 153 patients' cone beam computed tomography (CBCT) images were used. The segmentation method was employed to identify the MB2 canals in maxillary molars that had not previously had endodontic treatment. Labeled images were divided into training (80%), validation (10%) and testing (10%) groups. The artificial intelligence (AI) model was trained using the You Only Look Once v5 (YOLOv5x) architecture with 500 epochs and a learning rate of 0.01. Confusion matrix and receiver-operating characteristic (ROC) analysis were used in the statistical evaluation of the results. The sensitivity of the MB2 canal segmentation model was 0.92, the precision was 0.83, and the F1 score value was 0.87. The area under the curve (AUC) in the ROC graph of the model was 0.84. The mAP value at 0.5 inter-over union (IoU) was found as 0.88. The deep-learning algorithm used showed a high success in the detection of the MB2 canal. The success of the endodontic treatment can be increased and clinicians' time can be preserved using the newly created artificial intelligence-based models to identify variations in root canal anatomy before the treatment.

Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning.

BACKGROUND: Maxillofacial complex automated segmentation could alternative traditional segmentation methods to increase the effectiveness of virtual workloads. The use of DL systems in the detection of maxillary sinus and pathologies will both facilitate the work of physicians and be a support mechanism before the planned surgeries. OBJECTIVE: The aim was to use a modified You Only Look Oncev5x (YOLOv5x) architecture with transfer learning capabilities to segment both maxillary sinuses and maxillary sinus diseases on Cone-Beam Computed Tomographic (CBCT) images. METHODS: Data set consists of 307 anonymised CBCT images of patients (173 women and 134 males) obtained from the radiology archive of the Department of Oral and Maxillofacial Radiology. Bilateral maxillary sinuses CBCT scans were used to identify mucous retention cysts (MRC), mucosal thickenings (MT), total and partial opacifications, and healthy maxillary sinuses without any radiological features. RESULTS: Recall, precision and F1 score values for total maxillary sinus segmentation were 1, 0.985 and 0.992, respectively; 1, 0.931 and 0.964 for healthy maxillary sinus segmentation; 0.858, 0.923 and 0.889 for MT segmentation; 0.977, 0.877 and 0.924 for MRC segmentation; 1, 0.942 and 0.970 for sinusitis segmentation. CONCLUSION: This study demonstrates that maxillary sinuses can be segmented, and maxillary sinus diseases can be accurately detected using the AI model.

Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study.

BACKGROUND: Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future. METHODS: A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined. RESULTS: Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425. CONCLUSIONS: The model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm. CLINICAL REVELANCE: Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.

Classification of temporomandibular joint osteoarthritis on cone beam computed tomography images using artificial intelligence system.

BACKGROUND: The use of artificial intelligence has many advantages, especially in the field of oral and maxillofacial radiology. Early diagnosis of temporomandibular joint osteoarthritis by artificial intelligence may improve prognosis. OBJECTIVE: The aim of this study is to perform the classification of temporomandibular joint (TMJ) osteoarthritis and TMJ segmentation on cone beam computed tomography (CBCT) sagittal images with artificial intelligence. METHODS: In this study, the success of YOLOv5 architecture, an artificial intelligence model, in TMJ segmentation and osteoarthritis classification was evaluated on 2000 sagittal sections (500 healthy, 500 erosion, 500 osteophyte, 500 flattening images) obtained from CBCT DICOM images of 290 patients. RESULTS: The sensitivity, precision and F1 scores of the model for TMJ osteoarthritis classification are 1, 0.7678 and 0.8686, respectively. The accuracy value for classification is 0.7678. The prediction values of the classification model are 88% for healthy joints, 70% for flattened joints, 95% for joints with erosion and 86% for joints with osteophytes. The sensitivity, precision and F1 score of the YOLOv5 model for TMJ segmentation are 1, 0.9953 and 0.9976, respectively. The AUC value of the model for TMJ segmentation is 0.9723. In addition, the accuracy value of the model for TMJ segmentation was found to be 0.9953. CONCLUSION: Artificial intelligence model applied in this study can be a support method that will save time and convenience for physicians in the diagnosis of the disease with successful results in TMJ segmentation and osteoarthritis classification.

Supernumerary Teeth and Dental Development.

PURPOSE: The aims of this study were to compare the radiographic development of permanent teeth in a group of children with and without supernumerary teeth (ST), determine whether using cone-beam computed tomography or panoramic radiography improves the accuracy of dental age (DA) estimation and investigate the effects of factors including the numbers and positions of ST. METHODS: One hundred fifty dental radiographs of children with and without ST at the ages of 6.0 to 14.9 years were included in this study. The children in both groups were age and sex-matched. The lower left-side 7 permanent teeth were evaluated according to the Demirjian method, DA was determined. The difference between chronological age (CA) and DA (CA-DA) for the children with and without ST and further based on the number and localization of ST were calculated. RESULTS: For all groups, the mean DA values were higher than the mean CA values. The difference between the CA and DA values in the children with ST was higher than the difference in the children without ST. Supernumerary teeth in posterior localization, multiple ST and among boys were observed to increase the differences between the mean CA and DA values. The mean age difference between radiographies in the children with and without ST was similar. CONCLUSION: Panoramic radiography was found adequate in determination of dental development with the Demirjian method. Dental development was even more advanced in the children with ST in comparison to the control group. Clinicians should keep in mind that the dental developments of children with supernumerary teeth may be advanced.

Evaluation of Lefort I Osteotomy Line and Pterygomaxillary Junction Region in Patients With Cleft Lip and Palate.

OBJECTIVE: This study aimed to evaluate the Le Fort I osteotomy line and pterygomaxillary junction via cone-beam computed tomography in individuals with cleft lip and palate (CLP). DESIGN: Retrospective study. Patients and Methods: The study included individuals older than 16 years with CLP, who were scheduled for repositioning of the maxilla by Le Fort I osteotomy, and those with class III malocclusion with maxillary hypoplasia, who were scheduled for Le Fort I osteotomy. The measurements made in the area of the cleft of individuals with CLP were compared with both the side with no cleft and those with class III malocclusion with maxillary hypoplasia. A total of 11 measurements were made on the axial section parallel to the Frankfurt Horizontal plane, corresponding to the lower 1/5 of the distance between the infraorbital foramen and the anterior nasal spine. RESULTS: There were significant differences both in the comparisons made between the individuals with CLP and those without CLP in terms of the canal-anterior alveolar crest (G) and sinus-anterior alveolar crest (L) measurements (P < .05). The mean measurement values showed that the measurement results were higher in individuals with CLP in general. CONCLUSION: In conclusion, we believe that there might be difficulties both in osteotomy and down fracture stages during Le Fort I osteotomies performed in individuals with CLP.

Cone-Beam Computed Tomography Evaluation of the Eustachian Tube in Patients With Cleft Lip and Palate Compared With Normal Controls.

The authors compared the morphological features of the Eustachian tube (ET) between patients with cleft lip and palate (CL/P) and normal controls using cone-beam computed tomography (CBCT). CBCT images of 51 CL/P patients (28 males and 23 females, mean age: 18.5 ±â€Š8.0 years) and a control group of 52 patients (22 males and 30 females, mean age: 25.23 ±â€Š10.65 years) were retrospectively evaluated. The Eustachian tube angle (ETA), Eustachian tube length (EL), and auditory tube angle (ATA) were measured on CBCT images. The ETA, EL, and ATA in the CL/P and normal control groups were 30.4 ±â€Š6.2 and 36.7 ±â€Š7.5°; 24.7 ±â€Š3.7 and 27.7 ±â€Š4.3 mm; and 142.4 ±â€Š7.8 and 136.3 ±â€Š4.1°, respectively. All between-group differences were statistically significant (all P < 0.05). There were no significant between-gender differences in either group (all P > 0.05). Continuous variables were compared using the Mann-Whitney U-test. The morphological features of the ET, measured via multiplanar reconstruction CBCT, differed between CL/P patients and normal controls. CBCT can be used to evaluate ET morphological features.

Sphenoid sinus agenesis and sella turcica hypoplasia: very rare cases of two brothers with Hamamy syndrome.

Hamamy syndrome (HS) is an autosomal recessive syndrome with a genetic origin that is very rarely observed. The syndrome with craniofacial dysmorphisms, including midface prominence, severe telecanthus, sparse lateral eyebrows, protruding ears, fronto-nasal abnormalities, lacrimal-salivary apparatus agenesis, thin upper vermillion border, myopia, mental retardation, sensorineural hearing impairment, congenital heart anomalies with intraventricular conduction delay, hypochromic microcytic anaemia and skeletal abnormalities of the long bones with recurrent fractures. In this paper, we report a case of two brothers diagnosed with HS at the ages of 25 and 18 years, visited out clinic at different times due to dental reasons. In the radiological examinations, it was observed that both brothers have sphenoid sinuses agenesia, and their sella turcica were smaller than normal. HS may be observed very rarely, and it should be kept in mind that, in addition to various symptoms, it may also cause sphenoid sinus agenesis and sella turcica hypoplasia as shown for the first time in this case report.

Cone beam computed tomography imaging of superior semicircular canal morphology: a retrospective comparison of cleft lip/palate patients and normal controls.

OBJECTIVE: This study evaluated the prevalence and morphological characteristics of the superior semicircular canal (SSCC) in cleft lip and palate (CL/P) patients using cone beam computed tomography (CBCT). MATERIALS AND METHODS: CBCT images of 53 CL/P patients (28 males and 25 females) and a control group of 76 patients (42 males and 34 females) were evaluated. Retrospectively, 258 temporal bone images from 129 patients were evaluated in terms of SSCC morphology and divided into a normal pattern (0.6-1.7 mm in thickness), a papyraceous pattern (<0.5 mm), a thick pattern (>1.8 mm), a pneumatized pattern and dehiscent. The chi-squared test was used to compare differences among semicircular canal dehiscence (SSCD) patterns in the CL/P and control groups; p ≤ .05 was taken to reflect statistical significance. RESULTS: The characteristics of the SSCC were evaluated on CBCT images in patients with CL/P and controls. In total, 158 (61%) cases were normal (0.6-1.7 mm in thickness), 31 (12%) papyraceous (<0.5 mm), 8 (3%) thick, and 34 (13%) pneumatized. SSCD was observed in 27 (11%) cases. Statistically significant differences between the CL/P and control groups were evident in terms of SSCC morphology (p < .001). CONCLUSIONS: SSCD should be considered if a CL/P patient exhibits a vestibular system deficiency. Oral and maxillofacial radiologists should pay attention to SSCD when interpreting CBCT images. Future studies should use high-level spatial resolution CBCT to focus on cleft site and SSCC morphology in larger patient populations.

Morphometric Analysis of Sella Turcica Using Cone Beam Computed Tomography.

OBJECTIVE: The purpose of this study was to assess morphological shape and morphometric analysis of the sella turcica using cone beam computed tomography (CBCT) in different planes of section (coronal and sagittal). MATERIALS AND METHODS: CBCT images of 177 subjects of which 51 males and 126 females in the age group of 11 to 73 years were included in the study population. Linear dimensions which include the length, depth, diameter, and interclinoid distance were measured and the shape of sella turcica was analyzed. RESULTS: Sella turcica had circular morphology in 69.5% of the subjects while flattened shape of sella turcica was observed in 16.4%, oval shape of sella turcica in 14%. There was no significant difference in the all measurements of sella turcica between males and females (P > 0.05). Diameter (P < 0.01), depth (P < 0.001), length (P < 0.05), and interclinoid distance (P < 0.05) of the sella turcica differed significantly with age. CONCLUSIONS: The anatomical structure of sella turcica can be studied effectively in CBCT images. Linear dimensions and shape of sella turcica in the current study can be used as reference standards for further investigations.

Evaluation of Sella Turcica Shape and Dimensions in Cleft Subjects Using Cone-Beam Computed Tomography.

OBJECTIVE: The aim of this study was to assess the morphology of the sella turcica and measure its size in cleft and noncleft subjects. MATERIAL AND METHODS: Cone-beam computed tomography (CBCT) images of 54 individuals (29 males; 25 females) with cleft and 85 (22 males; 63 females) without cleft were used for this study. Syndromic patients with cleft(s) were not included because of possible additional endocrinological and/or morphological disorders. Linear measurements included length, depth, and diameter. The shape of the sella turcica was analyzed in the cleft and noncleft groups. An independent t test was conducted to evaluate differences between genders and groups. One-way ANOVA was used to compare age groups. RESULTS: The length (p < 0.001) of the sella turcica was smaller in noncleft subjects than in cleft subjects. Diameter (p = 0.014) and depth (p = 0.005) showed as constantly increasing from an age <15 to >25 years in the overall assessment. The distribution of the shape of the sella turcica differed significantly between groups (p < 0.001). CONCLUSIONS: In this study, CBCT was used to assess the morphology of the sella turcica. A majority of the subjects with cleft had a flattened sella turcica compared to that of the control group. A shorter length of the sella turcica was more evident in the cleft subjects than in the control group.

Comparative Evaluation of Temporomandibular Joint Parameters in Unilateral and Bilateral Cleft Lip and Palate Patients Using Cone-Beam CT: Focus on Growing vs. Non-Growing Subjects.

BACKGROUND: Morphological differences in the temporomandibular joint (TMJ) are crucial for the treatment of patients with cleft lip and palate (CLP). This study aims to evaluate and compare the TMJ parameters in patients with unilateral and bilateral CLP across growing and non-growing age groups using cone-beam computed tomography (CBCT). METHODS: CBCT records from 57 patients (23 males and 34 females) aged 6-50 years with a diagnosed unilateral or bilateral CLP were analyzed. Patients were categorized into four groups: growing unilateral (UGCLP), growing bilateral (BGCLP), non-growing unilateral (UNGCLP), and non-growing bilateral (BNGCLP). Measurements of TMJ parameters, including the mandibular fossa, articular eminence inclination, joint spaces, and roof thickness of the glenoid fossa, were conducted using CBCT images. RESULTS: Significant differences were observed in the anterior joint space (AJS) and the roof of the glenoid fossa (RGF) between growing and non-growing unilateral cleft patients. Additionally, significant discrepancies were found in the articular eminence angle when comparing the cleft and non-cleft sides within the unilateral growing group. No significant differences were observed in TMJ parameters between the right and left sides among bilateral cleft patients. CONCLUSIONS: The study highlights distinct TMJ morphological differences between growing and non-growing patients with CLP, emphasizing the importance of age-specific considerations in the treatment planning and growth monitoring of these patients.

A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs.

The study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry's Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success.

Morphometric and morphological evaluation of mastoid emissary canal using cone-beam computed tomography.

OBJECTIVES: This study aimed to determine mastoid emissary canal's (MEC) and mastoid foramen (MF) prevalence and morphometric characteristics on cone-beam computed tomography (CBCT) images to underline its clinical significance and discuss its surgical consequences. METHODS: In the retrospective analysis, two oral and maxillofacial radiologists analyzed the CBCT images of 135 patients (270 sides). The biggest MF and MEC were measured in the images evaluated in MultiPlanar Reconstruction (MPR) views. The MF and MEC mean diameters were calculated. The mastoid foramina number was recorded. The prevalence of MF was studied according to gender and side of the patient. RESULTS: The overall prevalence of MEC and MF was 119 (88.1%). The prevalence of MEC and MF is 55.5% in females and 44.5% in males. MEC and MF were identified as bilateral in 80 patients (67.20%) and unilateral in 39 patients (32.80%). The mean diameter of MF was 2.4 ± 0.9 mm. The mean height of MF was 2.3 ± 0.9. The mean diameter of the MEC was 2.1 ± 0.8, and the mean height of the MEC was 2.1 ± 0.8. There is a statistical difference between the genders (p = 0.043) in foramen diameter. Males had a significantly larger mean diameter of MF in comparison to females. CONCLUSION: MEC and MF must be evaluated thoroughly if the surgery is contemplated. Radiologists and surgeons should be aware of mastoid emissary canal morphology, variations, clinical relevance, and surgical consequences while operating in the suboccipital and mastoid areas to avoid unexpected and catastrophic complications. CBCT may be a reliable imaging diagnostic technique.

Nasopharynx evaluation in children of unilateral cleft palate patients and normal with cone beam computed tomography.

OBJECTIVE: This study aimed to examine the morphological characteristics of the nasopharynx in unilateral Cleft lip/palate (CL/P) children and non-cleft children using cone beam computed tomography (CBCT). METHODS: A retrospective study consisted of 54 patients, of which 27 patients were unilateral CL/P, remaining 27 patients have no CL/P. Eustachian tubes orifice (ET), Rosenmuller fossa (RF) depth, presence of pharyngeal bursa (PB), the distance of posterior nasal spine (PNS)-pharynx posterior wall were quantitatively evaluated. RESULTS: The main effect of the CL/P groups was found to be effective on RF depth-right (p < 0.001) and RF depth-left (p < 0.001). The interaction effect of gender and CL/P groups was not influential on measurements. The cleft-side main effect was found to be effective on RF depth-left (p < 0.001) and RF depth-right (p = 0002). There was no statistically significant relationship between CL/P groups and the presence of bursa pharyngea. CONCLUSIONS: Because it is the most common site of nasopharyngeal carcinoma (NPC), the anatomy of the nasopharynx should be well known in the early diagnosis of NPC.

Olfactory Fossa Evaluation as a Maxillary Sinus Development Using Cone Beam Computed Tomography.

In our study, we aimed to evaluate the risk of skull base perforation during endoscopic sinus surgery in individuals with hypoplasic maxillary sinuses using Cone Beam Computed Tomography (CBCT). We included 52 patients with bilateral maxillary sinus hypoplasia and 52 patients with normal maxillary sinus in the study. Reviewing paranasal CBCT scans, we noted the olfactory fossa depths and lateral lamella lengths of all the groups and compared between the hypoplasia groups and the control group. Compared the maxillary hypoplasia sinus individuals with the control group, both the olfactory fossa depths and the lateral lamina length were different in the maxillary hypoplasic individuals. In individuals with hypoplasic maxillary sinus, the olfactory fossa depth and the lateral lamina length values are higher, which increases the risk of complications in endoscopic sinus surgery.

Zygomaticocoronoid ankylosis with possible myositis ossificans: a very rare case.

Ankylosis forming between the zygomatic arch and the coronoid process is a rarely encountered pathological extracapsular ankylosis. Its treatment protocol consists of surgical removal of the coronoid process with the ankylotic mass and jaw opening-closing exercises after surgery. Myositis ossificans (MO) is a self-limiting, benign ossifying lesion. It affects all types of soft tissues including subcutaneous adipose tissue, muscles, tendons and nerves. It is most frequently found in the muscle as a solitary lesion. The clinical appearance of MO is generally in the form of a mass characterized with an ossified soft tissue. When it develops alone, cross-sectional imaging might not be specific, and it may appear similar to worse etiologies. It is suggested multiple imaging modalities should be used in the assessment of a suspicious soft tissue mass. MO is a benign self-limiting disease. In this case report, in the radiographic examination of a 41-year-old female patient, ankylosis between the left coronoid process and the zygomatic bone accompanied by possible MO in the left medial pterygoid muscle was observed. Resection of the coronoid process with the ipsilateral route, resection of the ankylotic mass with the hemicoronal approach and resection of the contralateral coronoid process with the intraoral approach were performed, but the ossified formation in the medial pterygoid muscle was not touched.

Automatic Feature Segmentation in Dental Periapical Radiographs.

While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.

Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images.

The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Türkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models. The sensitivity, precision, and F-measure values were 1.0, 1.0, and 1.0, respectively, for segmentation of sella turcica in sagittal slices of CBCT images. The sensitivity, precision, accuracy, and F1-score were 1.0, 0.95, 0.98, and 0.84, respectively, for sella-turcica-flattened classification; 0.95, 0.83, 0.92, and 0.88, respectively, for sella-turcica-oval classification; 0.75, 0.94, 0.90, and 0.83, respectively, for sella-turcica-round classification. It is predicted that detecting anatomical landmarks with orthodontic importance, such as the sella point, with artificial intelligence algorithms will save time for orthodontists and facilitate diagnosis.

Assesment of Prelacrimal Recess in Patients With Maxillary Sinus Hypoplasia Using Cone Beam Computed Tomography.

BACKGROUND: The prelacrimal recess approach, is frequently preferred in creating a minimally invasive surgical corridors. OBJECTIVE: The aim of this study was to evaluate the Prelacrimal recess (PLR) anatomy using Cone Beam Computed Tomography in patients with Maxillary Sinus Hypoplasia. METHODS: The paranasal Cone Beam Computed Tomography series of 84 adults were analyzed retrospectively. The antero-posterior and mesio-distal widths of the PLR and the antero-posterior width of the naso-lacrimal duct were measured. The patients were divided into three groups according to the antero-posterior width of PLR to evaluate the feasibility of prelacrimal recess approach as Type 1 (0-3 mm), Type 2 (>3-7 mm) and Type 3 (>7 mm). RESULTS: The mean antero-posterior width of PLR was 3.11 ± 1.49mm in the patients and 4.77 ± 1.76 mm in the controls. The mean mesio-distal width of PLR was 7.64 ± 1.49 mm in the patients and 3.17 ± 2.05 mm in the controls. The mean antero-posterior width of naso-lacrimal duct was 9.58 ± 2.80 mm in the patients and 9.46 ± 2.42 mm in the controls. CONCLUSIONS: The width of the antero-posterior PLR in patients with Maxillary Sinus Hypoplasia was found to be significantly lower in comparison to individuals with normal maxillary sinuses in the measurements performed on paranasal Cone Beam Computed Tomography scans. Hence, while planning a Functional Endoscopic Sinus Surgery with prelacrimal recess approach for maxillary sinus, the anatomical structure of the naso-sinusoidal region should be carefully analyzed, and individual anatomical variations such as Maxillary Sinus Hypoplasia should not be ignored.