GenCoder: A Novel Convolutional Neural Network Based Autoencoder for Genomic Sequence Data Compression.

Revolutionary advances in DNA sequencing technologies fundamentally change the nature of genomics. Today's sequencing technologies have opened into an outburst in genomic data volume. These data can be used in various applications where long-term storage and analysis of genomic sequence data are required. Data-specific compression algorithms can effectively manage a large volume of data. In recent times, deep learning has achieved great success in many compression tools and is gradually being used in genomic sequence compression. Significantly, autoencoder has been applied in dimensionality reduction, compact representations of data, and generative model learning. It can use convolutional layers to learn essential features from input data, which is better for image and series data. Autoencoder reconstructs the input data with some loss of information. Since accuracy is critical in genomic data, compressed genomic data must be decompressed without any information loss. We introduce a new scheme to address the loss incurred in the decompressed data of the autoencoder. This paper proposes a novel algorithm called GenCoder for reference-free compression of genomic sequences using a convolutional autoencoder and regenerating the genomic sequences from a latent code produced by the autoencoder, and retrieving original data losslessly. Performance evaluation is conducted on various genomes and benchmarked datasets. The experimental results on the tested data demonstrate that the deep learning model used in the proposed compression algorithm generalizes well for genomic sequence data and achieves a compression gain of 27% over the best state-of-the-art method.

Ectopic teeth with disparate migration: A literature review and new case series.

Purpose: Ectopic eruption can be defined as the emergence of a tooth in an abnormal location, where the tooth does not follow its typical eruption pathway. While ectopic eruption within the dentate region is well-documented in the literature, ectopic eruption in non-dentate regions is relatively rare. This study aimed to report 6 cases of ectopic teeth and present a systematic review of the English-language literature on ectopic teeth, emphasizing demographic characteristics, radiographic features, potential complications, and treatment options. Materials and Methods: A literature search was conducted using the PubMed, Medline, Web of Science, and Cochrane databases. The demographic data and radiographic findings of patients presenting with ectopic teeth were recorded. Results: The literature review yielded 61 cases of ectopic teeth, with patients ranging in age from 3 to 74 years. The findings from these previously reported cases demonstrated that the most common location for ectopic teeth was the maxillary sinus, which is consistent with this case series. The Pearson chi-square test was performed to evaluate the correlation between age and location of ectopic teeth, and the results were found to be statistically significant (P<0.05). However, no statistically significant relationship was observed between sex and the location of ectopic teeth. Conclusion: The distinct features of these cases warrant reporting. This study presents the first case of supernumerary teeth in the condyle without any associated pathosis. Another notable characteristic is the pre-eruptive resorption of 2 inverted supernumerary teeth ectopically located in the palate, which predisposes to sinus opacification.

Maxillary mucormycosis and concurrent osteomyelitis in a post-COVID-19 patient with new onset diabetes mellitus.

The occurrence of invasive fungal respiratory superinfections in patients with COVID-19 has gained much attention in the post-COVID era. The elucidation of invasive fungal sinusitis with osteomyelitis as a rare aggressive infection that requires prompt diagnosis and treatment to prevent fatal consequences has been noteworthy. Cone-beam computed tomography findings in those patients are central to early diagnosis and management. Here we report a case of post-COVID mucormycosis with osteomyelitis of the maxilla in a 72-year-old woman with a history of recently diagnosed diabetes mellitus.

Assessment of alveolar bone changes in response to minimally invasive periodontal surgery: A cone-beam computed tomographic evaluation.

Purpose: The aim of this study was to evaluate 3-dimensional cone-beam computed tomography (CBCT) images of alveolar bone changes in patients who underwent minimally invasive periodontal surgery-namely, the pinhole surgical technique (PST). Materials and Methods: Alveolar bone height was measured and compared on CBCT images of 254 teeth from 23 consecutive patients with Miller class I, II, or III recession who had undergone PST. No patient with active periodontal disease was selected for surgery. Two different methods were used to assess the alveolar bone changes postoperatively. In both methods, the distance between the apex of the tooth and the mid-buccal alveolar crestal bone on pre- and post-surgical CBCT studies was measured. Results: An average alveolar bone gain >0.5 mm following PST was identified using CBCT (P=0.05). None of the demographic variables, including sex, age, and time since surgery, had any significant effect on bone gain during follow-up, which ranged from 8 months to 3 years. Conclusion: PST appears to be a promising treatment modality for recession that results in stable clinical outcomes and may lead to some level of resolution on the bone level. More long-term studies must be done to evaluate the impact of this novel technique on bone remodeling and to assess sustained bone levels within a larger study population.

Incidental Findings of Asymptomatic Fungal Infection.

Fungal sinusitis of the paranasal sinuses is a rare infection in healthy individuals but is relatively common in immunocompromised patients. It is often misdiagnosed and frequently a severe disease, as a few forms of it are linked with a higher mortality rate. Effective handling necessitates a speedy analysis and often counts on radiological findings. On cone-beam computed tomography (CBCT) analysis, a bulky polypoid-shaped lesion with a density close to that of soft tissue in CBCT was visualized in the right ethmoid and sphenoid sinuses. There was a significant expansion of the borders of the right ethmoid sinus, and discontinuity or perforation of the sphenoid sinus floor was suspected from CBCT images. Non-contrasted multi-detector computed tomography (MDCT) exhibited opacification and extension of the lesion into the majority of sinuses with dense inspissated materials in the center, which resembled radiographic features of invasive fungal sinusitis. Computed tomography (CT) scan of the maxillofacial region, specifically paranasal sinuses, plays a considerable role in diagnosing fungal sinusitis. In a majority of cases, fungal sinusitis is noticed and diagnosed in immunocompromised patients. However, it is also seen in healthy patients in very rare circumstances, similar to the patient in this report. If the patient is treated rapidly, the prognosis is fair. We present a case of fungal sinusitis in an otherwise healthy young male patient to increase awareness among dental professionals.

Cone-beam computed tomographic imaging of central giant cell granuloma: A comprehensive review.

Purpose: The aim of this study was to characterize the cone-beam computed tomographic (CBCT) imaging features of central giant cell granuloma (CGCG) of the jawbone. Materials and Methods: This study retrospectively reviewed 26 CBCT studies of histologically proven cases of CGCG during a period of 20 years, from 1999 to 2019. Patients' demographic data were recorded, and radiographic features were assessed (location, border, cortication, appearance of the internal structure, locularity, septation, expansion, cortical perforation, effects on surrounding tissue, whether the lesion crossed the midline, and lesion volume). Results: In this study, CGCGs were seen almost twice as often in the mandible than in the maxilla, and 64.7% of mandibular lesions involved the anterior region. Only 26.9% of lesions crossed the midline, a feature that was considered characteristic of CGCG. Furthermore, 65.4% of lesions were unilocular and 34.6% were multilocular. The correlation between a lesion's size and its locularity was statistically significant, and larger lesions showed a multilocular appearance. The mean volume of multilocular lesions was greater than that of unilocular lesions. Conclusion: CGCGs showed variable radiographic features on CBCT, and this imaging modality is highly effective at demonstrating the radiographic spectrum and lesional extent of CGCGs in the jawbone.

A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network.

Several infectious diseases have affected the lives of many people and have caused great dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly discovered virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by the World Health Organisation in 2019. RT-PCR is considered the golden standard for COVID-19 detection. Due to the limited RT-PCR resources, early diagnosis of the disease has become a challenge. Radiographic images such as Ultrasound, CT scans, X-rays can be used for the detection of the deathly disease. Developing deep learning models using radiographic images for detecting COVID-19 can assist in countering the outbreak of the virus. This paper presents a computer-aided detection model utilizing chest X-ray images for combating the pandemic. Several pre-trained networks and their combinations have been used for developing the model. The method uses features extracted from pre-trained networks along with Sparse autoencoder for dimensionality reduction and a Feed Forward Neural Network (FFNN) for the detection of COVID-19. Two publicly available chest X-ray image datasets, consisting of 504 COVID-19 images and 542 non-COVID-19 images, have been combined to train the model. The method was able to achieve an accuracy of 0.9578 and an AUC of 0.9821, using the combination of InceptionResnetV2 and Xception. Experiments have proved that the accuracy of the model improves with the usage of sparse autoencoder as the dimensionality reduction technique.

Accuracy of a patient 3-dimensional virtual representation obtained from the superimposition of facial and intraoral scans guided by extraoral and intraoral scan body systems.

STATEMENT OF PROBLEM: A patient 3-dimensional virtual representation aims to facilitate the integration of facial references into treatment planning or prosthesis design procedures, but the accuracy of the virtual patient representation remains unclear. PURPOSE: The purpose of the present observational clinical study was to determine and compare the accuracy (trueness and precision) of a virtual patient obtained from the superimposition procedures of facial and intraoral digital scans guided by 2 scan body systems. MATERIAL AND METHODS: Ten participants were recruited. An intraoral digital scan was completed (TRIOS 4). Four fiduciary markers were placed in the glabella (Gb), left (IOL) and right infraorbital canal (IOR), and tip of the nose (TN). Two digitizing procedures were completed: cone beam computed tomography (CBCT) (i-CAT FLX V-Series) and facial scans (Face Camera Pro Bellus) with 2 different scan body systems: AFT (ScanBodyFace) and Sat 3D (Sat 3D). For the AFT system, a reference facial scan was obtained, followed by a facial scan with the participant in the same position as when capturing the CBCT scan. For the Sat 3D system, a reference facial scan was recorded, followed by a facial scan with the patient in the same position as when capturing the CBCT scan. The patient 3-dimensional representation for each scan body system was obtained by using a computer program (Matera 2.4). A total of 14 interlandmark distances were measured in the CBCT scan and both 3-dimensional patient representations. The discrepancies between the CBCT scan (considered the standard) and each 3-dimensional representation of each patient were used to analyze the data. The Kolmogorov-Smirnov test revealed that trueness and precision values were not normally distributed (P<.05). A log10 transformation was performed with 1-way repeated-measures MANOVA (α=.05). RESULTS: The accuracy of the virtual 3-dimensional patient representations obtained by using AFT and Sat 3D systems showed a trueness ranging from 0.50 to 1.64 mm and a precision ranging from 0.04 to 0.14 mm. The Wilks lambda detected an overall significant difference in the accuracy values between the AFT and Sat 3D systems (F=3628.041, df=14, P<.001). A significant difference was found in 12 of the 14 interlandmark measurements (P<.05). The AFT system presented significantly higher discrepancy values in Gb-IOL, TN-IOR, IOL-IOR, and TN-6 (P<.05) than in the Sat 3D system. The Sat 3D system had a significantly higher discrepancy in Gb-TN, TN-IOL, IOL-3, IOL-6, TN-8, TN-9, TN-11, IOR-11, and IOR-14 (P<.05) than in the AFT system. The Wilcoxon signed-rank test did not detect any significant difference in the precision values between the AFT and Sat 3D systems (Z=-0.838, P=.402). CONCLUSIONS: The accuracy of the patient 3-dimensional virtual representations obtained using AFT and Sat 3D systems showed trueness values ranging from 0.50 to 1.64 mm and precision values ranging from 0.04 to 0.14 mm. The AFT system obtained higher trueness than the Sat 3D system, but both systems showed similar precision values.

Computer-aided detection of COVID-19 from CT scans using an ensemble of CNNs and KSVM classifier.

Corona Virus Disease-2019 (COVID-19) is a global pandemic which is spreading briskly across the globe. The gold standard for the diagnosis of COVID-19 is viral nucleic acid detection with real-time polymerase chain reaction (RT-PCR). However, the sensitivity of RT-PCR in the diagnosis of early-stage COVID-19 is less. Recent research works have shown that computed tomography (CT) scans of the chest are effective for the early diagnosis of COVID-19. Convolutional neural networks (CNNs) are proven successful for diagnosing various lung diseases from CT scans. CNNs are composed of multiple layers which represent a hierarchy of features at each level. CNNs require a big number of labeled instances for training from scratch. In medical imaging tasks like the detection of COVID-19 where there is a difficulty in acquiring a large number of labeled CT scans, pre-trained CNNs trained on a huge number of natural images can be employed for extracting features. Feature representation of each CNN varies and an ensemble of features generated from various pre-trained CNNs can increase the diagnosis capability significantly. In this paper, features extracted from an ensemble of 5 different CNNs (MobilenetV2, Shufflenet, Xception, Darknet53 and EfficientnetB0) in combination with kernel support vector machine is used for the diagnosis of COVID-19 from CT scans. The method was tested using a public dataset and it attained an area under the receiver operating characteristic curve of 0.963, accuracy of 0.916, kappa score of 0.8305, F-score of 0.91, sensitivity of 0.917 and positive predictive value of 0.904 in the prediction of COVID-19.

NAS-SGAN: A Semi-Supervised Generative Adversarial Network Model for Atypia Scoring of Breast Cancer Histopathological Images.

Nuclear atypia scoring (NAS), forms a significant factor in determining individualized treatment plans and also for the prognosis of the disease. Automation of cancer grading using quantitative image-based analysis of histopathological images can circumvent the shortcomings of the prevailing manual grading and can assist the pathologists in cancer diagnosis. However, developing such a robust classifier model require sufficient amount of annotated data, while the labeled histopathological images are scarce and expensive to procure as annotation forms a time-consuming and laborious task. Hence, a semi-supervised learning framework combined with the deep neural network based generative adversarial training, that can improve the performance of the classification model with limited annotated data, is proposed in this paper. The proposed NAS-SGAN model consists of discriminator and generator models that are trained in an adversarial manner using both labeled and unlabeled samples. The discriminator model is designed as an unsupervised model stacked over the supervised model sharing the model parameters and learns the data distribution by extracting the discriminative features. The generator model is trained over a stable feature matching objective function following a composite GAN architecture, and its for the first time the semi-supervised GAN model is explored for the grading of breast cancer. Experimental analysis shows that the proposed model could better discriminate different cancer grades thereby improving the robustness and accuracy of the system, even with limited amount of labeled samples.

Evaluation of Cone-beam Computed Tomography Artifacts Produced by Metal Objects Located Within and Outside the Field of View.

INTRODUCTION: Artifacts created by the presence of metal objects in the jaw negatively affect the cone-beam computed tomographic image quality. This study compares artifacts produced by metal objects outside of the field of view (FOV) in a small FOV CBCT image with those produced in a large FOV image in which the metal object is within the FOV. METHODS: We methodically placed 4 titanium implant-sized rods and 4 zirconium crown-sized disks on 1 side of a human cadaver mandible. Using the Accuitomo 170 CBCT machine (J Morita, Irvine, CA), a total of 18 scans (9 with a small FOV and 9 with a large FOV) were made. Ten axial slices from each scan were transferred to ImageJ software (National Institutes of Health, Bethesda, MD) for analysis. The mean standard deviation of all voxel values of a fixed region of interest (ie, uniform air located lingual to tooth #30) was compared between small and large FOV slices. Two blinded observers subjectively rated the images for diagnostic quality and the presence of artifacts. RESULTS: The Wilcoxon signed rank test showed that the standard deviation for both small and large FOV slices increases as the number of metal objects increases. The mean of the standard deviation for small and large FOVs is 3.6 and 2.5, respectively (P = .0000). Fifty-three percent of the small FOV slices had more artifacts in the subjective analysis. One hundred percent of the small FOV slices were rated as higher quality. CONCLUSION: Metal objects outside of the FOV in the contralateral quadrant do affect the quality of small FOV images. However, small FOV images have a higher resolution compared with large FOV images.

Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier.

Corona virus disease-2019 (COVID-19) is a pandemic caused by novel coronavirus. COVID-19 is spreading rapidly throughout the world. The gold standard for diagnosing COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR) test. However, the facility for RT-PCR test is limited, which causes early diagnosis of the disease difficult. Easily available modalities like X-ray can be used to detect specific symptoms associated with COVID-19. Pre-trained convolutional neural networks are widely used for computer-aided detection of diseases from smaller datasets. This paper investigates the effectiveness of multi-CNN, a combination of several pre-trained CNNs, for the automated detection of COVID-19 from X-ray images. The method uses a combination of features extracted from multi-CNN with correlation based feature selection (CFS) technique and Bayesnet classifier for the prediction of COVID-19. The method was tested using two public datasets and achieved promising results on both the datasets. In the first dataset consisting of 453 COVID-19 images and 497 non-COVID images, the method achieved an AUC of 0.963 and an accuracy of 91.16%. In the second dataset consisting of 71 COVID-19 images and 7 non-COVID images, the method achieved an AUC of 0.911 and an accuracy of 97.44%. The experiments performed in this study proved the effectiveness of pre-trained multi-CNN over single CNN in the detection of COVID-19.

Large airway-obstructing retropharyngeal lipoma in an asymptomatic patient: a case report.

BACKGROUND: Lipomas are common benign mesenchymal tumors that appear in the head and neck region in approximately 25% of cases where they are noted. Lipomas of the airway region are exceedingly rare, accounting for less than 1% of airway obstruction tumors. Correlation of radiographic findings from cone beam computed tomography (CBCT), multi-detector computed tomography (MDCT), and magnetic resonance imaging (MRI) of a rare retropharyngeal lipoma has not been previously reported. CBCT studies acquired for implant and/or other diagnostic purposes may be the first line of detection as an incidental finding. CASE PRESENTATION: A 66-year-old female presented for a pre-implant CBCT with no history of other complaints or signs/symptoms. CBCT imaging depicts a large, well-defined, low-attenuation/soft tissue density lesion with an undulating appearance extending from the posterior left pharyngeal wall and occluding two-thirds of the airway from C2 to C4. The lesion extends laterally into the left parapharyngeal space and inferiorly beyond the field of view of the study. Evidence of faint internal septations was noted. The patient was immediately referred for an ENT consult. Laryngoscopy, MRI, and contrast-enhanced MDCT imaging were conducted to determine the full extent and nature of the lesion, as well as to potentially plan for biopsy and/or surgical resection. Removal of the lesion was successful, and histopathologic evaluation confirmed lipoma. Periodic follow-up was recommended to monitor for possible recurrence. DISCUSSION: The slower growth pattern of some benign lesions may obscure any symptoms as changes the patient may normally notice take place over an extended period. Furthermore, soft tissue lesions and especially those in the posterior midline, such as in this case, may not be easily visible on routine panoramic imaging or clinical exam, allowing for substantially large growth before detection. While the soft tissue contrast of the CBCT volume is poor, enough information was present to establish an initial differential diagnosis and the need for more advanced imaging modalities. With the growing popularity and adoption of CBCT in maxillofacial imaging, a thorough understanding of the appearance of hard and soft tissue lesions, as well as a strong understanding of the baseline appearance of normal anatomy, is important to ensure no incidental pathoses go undiagnosed.

Batch Mode Active Learning on the Riemannian Manifold for Automated Scoring of Nuclear Pleomorphism in Breast Cancer.

Breast cancer is the most prevalent invasive type of cancer among women. The mortality rate of the disease can be reduced considerably through timely prognosis and felicitous treatment planning, by utilizing the computer aided detection and diagnosis techniques. With the advent of whole slide image (WSI) scanners for digitizing the histopathological tissue samples, there is a drastic increase in the availability of digital histopathological images. However, these samples are often unlabeled and hence they need labeling to be done through manual annotations by domain experts and experienced pathologists. But this annotation process required for acquiring high quality large labeled training set for nuclear atypia scoring is a tedious, expensive and time consuming job. Active learning techniques have achieved widespread acceptance in reducing this human effort in annotating the data samples. In this paper, we explore the possibilities of active learning on nuclear pleomorphism scoring over a non-Euclidean framework, the Riemannian manifold. Active learning technique adopted for the cancer grading is in the batch-mode framework, that adaptively identifies the apt batch size along with the batch of instances to be queried, following a submodular optimization framework. Samples for annotation are selected considering the diversity and redundancy between the pair of samples, based on the kernelized Riemannian distance measures such as log-Euclidean metrics and the two Bregman divergences - Stein and Jeffrey divergences. Results of the adaptive Batch Mode Active Learning on the Riemannian metric show a superior performance when compared with the state-of-the-art techniques for breast cancer nuclear pleomorphism scoring, as it makes use of the information from the unlabeled samples.

Computer-Aided Histopathological Image Analysis Techniques for Automated Nuclear Atypia Scoring of Breast Cancer: a Review.

Breast cancer is the most common type of malignancy diagnosed in women. Through early detection and diagnosis, there is a great chance of recovery and thereby reduce the mortality rate. Many preliminary tests like non-invasive radiological diagnosis using ultrasound, mammography, and MRI are widely used for the diagnosis of breast cancer. However, histopathological analysis of breast biopsy specimen is inevitable and is considered to be the golden standard for the affirmation of cancer. With the advancements in the digital computing capabilities, memory capacity, and imaging modalities, the development of computer-aided powerful analytical techniques for histopathological data has increased dramatically. These automated techniques help to alleviate the laborious work of the pathologist and to improve the reproducibility and reliability of the interpretation. This paper reviews and summarizes digital image computational algorithms applied on histopathological breast cancer images for nuclear atypia scoring and explores the future possibilities. The algorithms for nuclear pleomorphism scoring of breast cancer can be widely grouped into two categories: handcrafted feature-based and learned feature-based. Handcrafted feature-based algorithms mainly include the computational steps like pre-processing the images, segmenting the nuclei, extracting unique features, feature selection, and machine learning-based classification. However, most of the recent algorithms are based on learned features, that extract high-level abstractions directly from the histopathological images utilizing deep learning techniques. In this paper, we discuss the various algorithms applied for the nuclear pleomorphism scoring of breast cancer, discourse the challenges to be dealt with, and outline the importance of benchmark datasets. A comparative analysis of some prominent works on breast cancer nuclear atypia scoring is done using a benchmark dataset which enables to quantitatively measure and compare the different features and algorithms used for breast cancer grading. Results show that improvements are still required, to have an automated cancer grading system suitable for clinical applications.

Cone-beam computed tomographic imaging of silent sinus syndrome: A case series and a literature review.

While silent sinus syndrome (SSS) is familiar to otolaryngologists and ophthalmologists, it is a rare clinical entity in dentistry and is likely to be underdiagnosed due to dentists' lack of awareness of this condition. SSS presents a diagnostic challenge to dentists, as patients typically have no history of trauma or sinusitis. The characteristic feature of SSS is a gradual retreat of the maxillary sinus walls, resulting in enophthalmos and hypoglobus. Multidetector (multislice) computed tomography is the imaging modality of choice for SSS and other paranasal sinus diseases. Cone-beam computed tomography promises to be an alternative low-dose imaging modality. This report describes 3 cases of SSS in adults, who had no identified clinical symptoms except diminutive and opacified maxillary sinuses, as well as the inward bowing of the sinus walls as noted on cone-beam computed tomographic imaging.

Domain independent redundancy elimination based on flow vectors for static video summarization.

Video summarization aims to find a compact representation of input videos. The method finds out interesting parts of the video by discarding the remaining parts of the video. The abstracts thus generated enhances browsing and retrieval of video data. The quality of summaries generated by video summarization algorithms can be improved if the redundant frames in the input video are taken care of before summarization. This paper presents a novel domain-independent method for redundancy elimination from input videos before summarization maintaining keyframes in the original video. The frames of input video are first presampled by selecting two frames in one second. The flow vectors between consecutive frames are computed using SIFT Flow algorithm. The magnitude of flow vectors at each pixel position of the frame are summed up to find the displacement magnitude between the consecutive frames. The redundant frames are filtered out based on local averaging of the displacement values. The evaluation of the method is done using two standard datasets namely VSUMM and OVP. The results demonstrate that an average reduction rate of 97.64% is achieved consistently on videos of all categories. The method also gives superior results compared to other state-of-the-art redundancy elimination methods for video summarization.

Radiographic manifestations of fibroblastic osteosarcoma: A diagnostic challenge.

Osteosarcoma is the most common primary bone tumor after plasma cell neoplasms. Osteosarcoma has diverse histological features and is characterized by the presence of malignant spindle cells and pluripotent neoplastic mesenchymal cells that produce immature bone, cartilage, and fibrous tissue. Osteosarcoma most frequently develops in the extremities of long bones, but can occur in the jaw in rare cases. The clinical and biological behavior of osteosarcoma of the jaw slightly differs from that of long-bone osteosarcoma. The incidence of jaw osteosarcoma is greater in the third to fourth decades of life, whereas long-bone osteosarcoma mostly occurs in the second decade of life. Osteosarcoma of the jaw has a lower tendency to metastasize and a better prognosis than long-bone osteosarcoma. Radiographically, osteosarcoma can present as a poorly-defined lytic, sclerotic, or mixed-density lesion with periosteal bone reaction response. Multi-detector computed tomography is useful for identifying the extent of bone destruction, as well as soft tissue involvement of the lesion. The current case report presents a fibroblastic osteosarcoma involving the left hemimandible with very unusual radiographic features.

A Novel Prefabricated Grid for Guided Endodontic Microsurgery.

INTRODUCTION: This study aimed to introduce a novel method using cone-beam computed tomographic (CBCT) imaging and prefabricated grids to guide apical access during endodontic microsurgery and to compare its accuracy with that of the nonguided method. METHODS: Forty-two roots from human cadaver jaws were selected. Twenty-one were randomly assigned to the experimental group (grid based) and their contralateral counterparts to the control group (nonguided). Preoperative CBCT images were used to design a drill path that intended to reach the palatal/lingual aspect of the roots without attempting to complete the osteotomy or to resect the entire root end. In the experimental group, prefabricated metal grids used during imaging and surgery acted as a reference in the design and drilling. Postoperative CBCT volumes were superimposed on the preoperative volumes, and the distances between the actual drill paths and the target points were measured. A dichotomized outcome of success versus failure was also recorded and compared. Statistical analysis was performed using the paired t test and Fisher exact test. RESULTS: The mean deviation of the drill paths from the target points was 0.66 mm ± 0.54 mm (mean ± standard deviation) for grid-based drilling and 1.92 mm ± 1.05 mm (mean ± standard deviation) for nonguided drilling (P < .001). Grid-based drilling was on average 1.27 mm (95% confidence interval, 0.81-1.72 mm) closer to the target point than nonguided drilling. The probability of successful drilling was also significantly higher with grids than without grids (P = .02). CONCLUSIONS: The proposed method of guided osteotomy and root-end resection using prefabricated grids was more accurate than the nonguided method.