Ameloblastoma in a Three-Year-Old Child with Hurler Syndrome (Mucopolysaccharidosis Type I)

Bartolomeo, Mattia Di; Pellacani, Arrigo; Negrello, Sara; Buchignani, Martina; Nocini, Riccardo; Massa, G; Gianotti, Greta; Pollastri, Giuseppe; Colletti, Giacomo; Chiarini, Luigi; Anesi, Alexandre

doi:10.3390/reports5010010

Cited by 2 publications

(1 citation statement)

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“…The differential diagnosis for patients presenting with lytic lesions is broad and depends on the patient's age and the presence of other systemic disease [22][23][24].…”

Section: Discussionmentioning

confidence: 99%

Primary Mandibular Condyle Xanthoma: Case Report and Literature Review

Negrello

Pellacani

Bartolomeo

et al. 2023

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Bone xanthoma is a rare benign primary bone lesion histologically characterized by sheets of foamy cells which are macrophages with a cytoplasm filled with droplets of fat. It is usually associated with endocrine or metabolic diseases, in the absence of which the lesion is called primary xanthoma. Because of the lack of pathognomonic radiologic and clinical features, they require a differential diagnosis with a broad spectrum of lesions with a varying degree of malignant potential. We describe a case of primary mandibular xanthoma of a 16-year-old girl without typical cutaneous manifestations and alterations in lipid values. The temporomandibular joint involvement at the mandibular condyle is peculiar in the case described here. We present even a qualitative systematic review of the literature on primary xanthoma of the jaws in others to draw up treatment guidelines.

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“…The differential diagnosis for patients presenting with lytic lesions is broad and depends on the patient's age and the presence of other systemic disease [22][23][24].…”

Section: Discussionmentioning

confidence: 99%

Primary Mandibular Condyle Xanthoma: Case Report and Literature Review

Negrello

Pellacani

Bartolomeo

et al. 2023

Reports

Self Cite

View full text Add to dashboard Cite

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Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm

et al. 2023

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Introduction: The need of accurate three-dimensional data of anatomical structures is increasing in the surgical field. The development of convolutional neural networks (CNNs) has been helping to fill this gap by trying to provide efficient tools to clinicians. Nonetheless, the lack of a fully accessible datasets and open-source algorithms is slowing the improvements in this field. In this paper, we focus on the fully automatic segmentation of the Inferior Alveolar Canal (IAC), which is of immense interest in the dental and maxillo-facial surgeries. Conventionally, only a bidimensional annotation of the IAC is used in common clinical practice. A reliable convolutional neural network (CNNs) might be timesaving in daily practice and improve the quality of assistance. Materials and methods: Cone Beam Computed Tomography (CBCT) volumes obtained from a single radiological center using the same machine were gathered and annotated. The course of the IAC was annotated on the CBCT volumes. A secondary dataset with sparse annotations and a primary dataset with both dense and sparse annotations were generated. Three separate experiments were conducted in order to evaluate the CNN. The IoU and Dice scores of every experiment were recorded as the primary endpoint, while the time needed to achieve the annotation was assessed as the secondary end-point. Results: A total of 347 CBCT volumes were collected, then divided into primary and secondary datasets. Among the three experiments, an IoU score of 0.64 and a Dice score of 0.79 were obtained thanks to the pre-training of the CNN on the secondary dataset and the creation of a novel deep label propagation model, followed by proper training on the primary dataset. To the best of our knowledge, these results are the best ever published in the segmentation of the IAC. The datasets is publicly available and algorithm is published as open-source software. On average, the CNN could produce a 3D annotation of the IAC in 6.33 s, compared to 87.3 s needed by the radiology technician to produce a bidimensional annotation. Conclusions: To resume, the following achievements have been reached. A new state of the art in terms of Dice score was achieved, overcoming the threshold commonly considered of 0.75 for the use in clinical practice. The CNN could fully automatically produce accurate three-dimensional segmentation of the IAC in a rapid setting, compared to the bidimensional annotations commonly used in the clinical practice and generated in a time-consuming manner. We introduced our innovative deep label propagation method to optimize the performance of the CNN in the segmentation of the IAC. For the first time in this field, the datasets and the source codes used were publicly released, granting reproducibility of the experiments and helping in the improvement of IAC segmentation.

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Ameloblastoma in a Three-Year-Old Child with Hurler Syndrome (Mucopolysaccharidosis Type I)

Cited by 2 publications

References 44 publications

Primary Mandibular Condyle Xanthoma: Case Report and Literature Review

Primary Mandibular Condyle Xanthoma: Case Report and Literature Review

Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm

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