Purpose: This study was performed to assess the performance and accuracy of artificial intelligence (AI) in the detection and diagnosis of maxillary sinus pathologies using computed tomography (CT)/cone-beam computed tomography (CBCT) imaging. Materials and Methods: A comprehensive literature search was conducted across 4 databases: Google Scholar, BioMed Central (BMC), ProQuest, and PubMed. Combinations of keywords such as “DCNN,” “deep learning,” “convolutional neural network,” “machine learning,” “predictive modeling,” and “data mining” were used to identify relevant articles. The study included articles that were published within the last 5 years, written in English, available in full text, and focused on diagnostic accuracy. Results: Of an initial 530 records, 12 studies with a total of 3,349 patients (7,358 images) were included. All articles employed deep learning methods. The most commonly tested pathologies were maxillary rhinosinusitis and maxillary sinusitis, while the most frequently used AI models were convolutional neural network architectures, including ResNet and DenseNet, YOLO, and U-Net. DenseNet and ResNet architectures have demonstrated superior precision in detecting maxillary sinus pathologies due to their capacity to handle deeper networks without overfitting. The performance in detecting maxillary sinus pathology varied, with an accuracy ranging from 85% to 97%, a sensitivityof 87% to 100%, a specificity of 87.2% to 99.7%, and an area under the curve of 0.80 to 0.91. Conclusion AI with various architectures has been used to detect maxillary sinus abnormalities on CT/CBCT images, achieving near-perfect results. However, further improvements are needed to increase accuracy and consistency.

Background/Aims: Behçet’s disease (BD) is an autoinflammatory disease of unknown etiopathogenesis. Oxylipins i.e., prostaglandins, leukotrienes, lipoxins, resolvins, and protectins are bioactive polyunsaturated fatty acids (PUFAs) derivatives involved in inflammatory response induction and resolution. The study aimed to determine the profile of selected PUFAs and oxylipins and to define a lipidomic signature for BD. Methods: A case-control study was conducted involving thirty-five patients with BD and thirty-five age and sex-matched healthy individuals as a control group. Selected plasma PUFAs and oxylipins were analyzed using a targeted LC-MS/MS method. Results: The lipidomic profile was different between the two groups. BD patients showed higher levels of oxylipins deriving from either the n-6-arachidonic acid (i.e., prostaglandin D2, E2, F2α, and 6-keto-F1α, thromboxane B2, leukotriene B4, E4 and F4, and 6-epi and 15-epi-lipoxin A4) or n-3 PUFAs (i.e., 18-hydroxyeicosapentaenoic acid, 7,17-dihydroxy docosapentaenoic acid, protectin X, and resolvin D5), but decreased levels of both n-3 and n-6 PUFAs. Multivariate analyses selected the combination of four mediators, i.e., docosapentaenoic acid, prostaglandin E2, thromboxane B2, and lipoxin A4 as an accurate lipidomic signature for BD. Conclusions The profile of PUFAs/oxylipins is altered in BD patients, characterized by increased pro-inflammatory and pro-resolving oxylipins. The findings suggest that oxylipin metabolism might be involved in BD pathophysiology and may represent a therapeutic target for the disease. Further research is required to examine the role of lipid mediators in BD.

Purpose: Despite advances in the treatment of adult acute lymphoblastic leukemia (ALL), relapse remains the most significant challenge in improving prognosis. Measurable residual disease (MRD) assessment can predict bone marrow relapse based on MRD positivity. As access to innovative therapies remains limited because of the high cost, chemotherapy is the widely utilized treatment option. The efficacy of a combination of bortezomib and Hyper-CVAD has been reported in patients with multiple myeloma; however, its efficacy has not yet been confirmed in patients with ALL. Methods: This prospective cohort study involved patients with ALL who presented with MRD-positive results or relapse and received treatment with a combination of bortezomib and Hyper-CVAD at two reference centers in Mexico City. Results: Of the 20 patients with positive MRD included in this study, 60% (n = 12) exhibited MRD negative results after combination treatment, 30% (n = 6) persisted positive MRD results, and 10% (n = 2) passed away. Of the 23 patients with bone marrow relapse, 43.5% (n = 10) achieved a second complete remission (2CR), 34.8% (n = 6) exhibited refractory status, and 21.7% (n = 5) passed away. To achieve a 2CR, 20% (n = 2) patients required less than four cycles of treatment, 50% (n = 5) required four cycles (two A and B cycles each), and 30% (n = 3) required six cycles. Conclusion The combination of bortezomib and Hyper-CVAD treatment exhibited better results in achieving MRD negative results, indicating its potential as a promising first-line treatment strategy for ALL.

Background: Pneumatization of turbinates, also known as concha bullosa (CB), is associated with nasal septal deviation and sinonasal pathologies. This study aims to evaluate the performance of deep learning models in detecting CB in coronal cone-beam computed tomography (CBCT) images. Methods: Standardized coronal images were obtained from 203 CBCT scans (83 with CB and 119 without CB) from the radiology archives of a dental teaching hospital. These scans underwent preprocessing through a hybridized contrast enhancement (CE) method using discrete wavelet transform (DWT). Of the 203 CBCT images, 162 were randomly assigned to the training set and 41 to the testing set. Initially, the images were enhanced using a CE technique before being input into pre-trained deep learning models, namely ResNet50, ResNet101, and MobileNet. The features extracted by each model were then flattened and input into a random forest (RF) classifier. In the subsequent phase, the CE technique was refined by incorporating DWT. Results: CE-DWT-ResNet101-RF demonstrated the highest performance, achieving an accuracy of 91.7% and an area under the curve (AUC) of 98%. In contrast, CE-MobileNet-RF recorded the lowest accuracy at 82.46% and an AUC of 92%. The highest precision, recall, and F1 score (all 92%) were observed for CE-DWT-ResNet101-RF. Conclusion Deep learning models demonstrated high accuracy in detecting CB in CBCT images. However, to confirm these results, further studies involving larger sample sizes and various deep learning models are required.

Background/Aims: Behçet’s disease (BD) is an autoinflammatory disease of unknown etiopathogenesis. Oxylipins i.e., prostaglandins, leukotrienes, lipoxins, resolvins, and protectins are bioactive polyunsaturated fatty acids (PUFAs) derivatives involved in inflammatory response induction and resolution. The study aimed to determine the profile of selected PUFAs and oxylipins and to define a lipidomic signature for BD. Methods: A case-control study was conducted involving thirty-five patients with BD and thirty-five age and sex-matched healthy individuals as a control group. Selected plasma PUFAs and oxylipins were analyzed using a targeted LC-MS/MS method. Results: The lipidomic profile was different between the two groups. BD patients showed higher levels of oxylipins deriving from either the n-6-arachidonic acid (i.e., prostaglandin D2, E2, F2α, and 6-keto-F1α, thromboxane B2, leukotriene B4, E4 and F4, and 6-epi and 15-epi-lipoxin A4) or n-3 PUFAs (i.e., 18-hydroxyeicosapentaenoic acid, 7,17-dihydroxy docosapentaenoic acid, protectin X, and resolvin D5), but decreased levels of both n-3 and n-6 PUFAs. Multivariate analyses selected the combination of four mediators, i.e., docosapentaenoic acid, prostaglandin E2, thromboxane B2, and lipoxin A4 as an accurate lipidomic signature for BD. Conclusions The profile of PUFAs/oxylipins is altered in BD patients, characterized by increased pro-inflammatory and pro-resolving oxylipins. The findings suggest that oxylipin metabolism might be involved in BD pathophysiology and may represent a therapeutic target for the disease. Further research is required to examine the role of lipid mediators in BD.

Background/Aims: Behçet’s disease (BD) is an autoinflammatory disease of unknown etiopathogenesis. Oxylipins i.e., prostaglandins, leukotrienes, lipoxins, resolvins, and protectins are bioactive polyunsaturated fatty acids (PUFAs) derivatives involved in inflammatory response induction and resolution. The study aimed to determine the profile of selected PUFAs and oxylipins and to define a lipidomic signature for BD. Methods: A case-control study was conducted involving thirty-five patients with BD and thirty-five age and sex-matched healthy individuals as a control group. Selected plasma PUFAs and oxylipins were analyzed using a targeted LC-MS/MS method. Results: The lipidomic profile was different between the two groups. BD patients showed higher levels of oxylipins deriving from either the n-6-arachidonic acid (i.e., prostaglandin D2, E2, F2α, and 6-keto-F1α, thromboxane B2, leukotriene B4, E4 and F4, and 6-epi and 15-epi-lipoxin A4) or n-3 PUFAs (i.e., 18-hydroxyeicosapentaenoic acid, 7,17-dihydroxy docosapentaenoic acid, protectin X, and resolvin D5), but decreased levels of both n-3 and n-6 PUFAs. Multivariate analyses selected the combination of four mediators, i.e., docosapentaenoic acid, prostaglandin E2, thromboxane B2, and lipoxin A4 as an accurate lipidomic signature for BD. Conclusions The profile of PUFAs/oxylipins is altered in BD patients, characterized by increased pro-inflammatory and pro-resolving oxylipins. The findings suggest that oxylipin metabolism might be involved in BD pathophysiology and may represent a therapeutic target for the disease. Further research is required to examine the role of lipid mediators in BD.

Purpose: This study aims to validate the use of ProAnalyst (Xcitex Inc.), a software for professional motion analysts to assess the performance of surgical interns while performing the peg transfer task in a simulator box for safe practice in real minimally invasive surgery. Methods: A correlation study was conducted in a multidisciplinary skills simulation lab at the Faculty of Medicine, Jordan University of Science and Technology from October 2019 to February 2020. Forty-one interns (i.e., novices and intermediates) were recruited and an expert surgeon participated as a reference benchmark. Videos of participants’ performance were analyzed through the ProAnalyst and Global Operative Assessment of Laparoscopic Skills (GOALS). Two results were s analyzed for correlation. Results: The motion analysis scores by Proanalyst were correlated with those by GOALS for novices (r=–0.62925, P=0.009), and Intermediates (r= –0.53422, P=0.033). Both assessment methods differentiated the participants’ performance based on their experience level. Conclusion The motion analysis scoring method with Proanalyst provides an objective, time-efficient, and reproducible assessment of interns’ performance, and comparable to GOALS. It may require initial training and set-up; however, it eliminates the need for expert surgeon judgment.

Background: Pneumatization of turbinates, also known as concha bullosa (CB), is associated with nasal septal deviation and sinonasal pathologies. This study aims to evaluate the performance of deep learning models in detecting CB in coronal cone-beam computed tomography (CBCT) images. Methods: Standardized coronal images were obtained from 203 CBCT scans (83 with CB and 119 without CB) from the radiology archives of a dental teaching hospital. These scans underwent preprocessing through a hybridized contrast enhancement (CE) method using discrete wavelet transform (DWT). Of the 203 CBCT images, 162 were randomly assigned to the training set and 41 to the testing set. Initially, the images were enhanced using a CE technique before being input into pre-trained deep learning models, namely ResNet50, ResNet101, and MobileNet. The features extracted by each model were then flattened and input into a random forest (RF) classifier. In the subsequent phase, the CE technique was refined by incorporating DWT. Results: CE-DWT-ResNet101-RF demonstrated the highest performance, achieving an accuracy of 91.7% and an area under the curve (AUC) of 98%. In contrast, CE-MobileNet-RF recorded the lowest accuracy at 82.46% and an AUC of 92%. The highest precision, recall, and F1 score (all 92%) were observed for CE-DWT-ResNet101-RF. Conclusion Deep learning models demonstrated high accuracy in detecting CB in CBCT images. However, to confirm these results, further studies involving larger sample sizes and various deep learning models are required.

Purpose: Despite advances in the treatment of adult acute lymphoblastic leukemia (ALL), relapse remains the most significant challenge in improving prognosis. Measurable residual disease (MRD) assessment can predict bone marrow relapse based on MRD positivity. As access to innovative therapies remains limited because of the high cost, chemotherapy is the widely utilized treatment option. The efficacy of a combination of bortezomib and Hyper-CVAD has been reported in patients with multiple myeloma; however, its efficacy has not yet been confirmed in patients with ALL. Methods: This prospective cohort study involved patients with ALL who presented with MRD-positive results or relapse and received treatment with a combination of bortezomib and Hyper-CVAD at two reference centers in Mexico City. Results: Of the 20 patients with positive MRD included in this study, 60% (n = 12) exhibited MRD negative results after combination treatment, 30% (n = 6) persisted positive MRD results, and 10% (n = 2) passed away. Of the 23 patients with bone marrow relapse, 43.5% (n = 10) achieved a second complete remission (2CR), 34.8% (n = 6) exhibited refractory status, and 21.7% (n = 5) passed away. To achieve a 2CR, 20% (n = 2) patients required less than four cycles of treatment, 50% (n = 5) required four cycles (two A and B cycles each), and 30% (n = 3) required six cycles. Conclusion The combination of bortezomib and Hyper-CVAD treatment exhibited better results in achieving MRD negative results, indicating its potential as a promising first-line treatment strategy for ALL.

Purpose: This study was performed to assess the performance and accuracy of artificial intelligence (AI) in the detection and diagnosis of maxillary sinus pathologies using computed tomography (CT)/cone-beam computed tomography (CBCT) imaging. Materials and Methods: A comprehensive literature search was conducted across 4 databases: Google Scholar, BioMed Central (BMC), ProQuest, and PubMed. Combinations of keywords such as “DCNN,” “deep learning,” “convolutional neural network,” “machine learning,” “predictive modeling,” and “data mining” were used to identify relevant articles. The study included articles that were published within the last 5 years, written in English, available in full text, and focused on diagnostic accuracy. Results: Of an initial 530 records, 12 studies with a total of 3,349 patients (7,358 images) were included. All articles employed deep learning methods. The most commonly tested pathologies were maxillary rhinosinusitis and maxillary sinusitis, while the most frequently used AI models were convolutional neural network architectures, including ResNet and DenseNet, YOLO, and U-Net. DenseNet and ResNet architectures have demonstrated superior precision in detecting maxillary sinus pathologies due to their capacity to handle deeper networks without overfitting. The performance in detecting maxillary sinus pathology varied, with an accuracy ranging from 85% to 97%, a sensitivityof 87% to 100%, a specificity of 87.2% to 99.7%, and an area under the curve of 0.80 to 0.91. Conclusion AI with various architectures has been used to detect maxillary sinus abnormalities on CT/CBCT images, achieving near-perfect results. However, further improvements are needed to increase accuracy and consistency.