Radiographic increase in the size of tibial and femoral tunnels has been observed following the reconstruction of the ACL with a bone-patellar tendon-bone autograft. The purpose of this study is to determine if any differences exist in the amount of enlargement of the bone tunnel with the clinical results and to know the factors which affected to the enlargement of the bone tunnels. Total 27 patients were retrospectively reviewed for tunnel enlargement radiographically at one year after operation. Anteroposterior and lateral x-ray were obtained and the tunnel were measured by two independent observers. The measurements were made at the widest part of the tunnel. The distance between tibial interference screw and knee joint line also measured. Correction for magnification was performed by comparing the measured width of the interference screw used for fixation of the graft with its actual width. Statistical analysis was performed with Wilcoxon rank sum test. The radiographic tunnel enlargement was an average of 1.7+/-1.3mm for the femur and 1.9+/-0.8mm for the tibia. The proximal migration of the tibial interference screw was an average of 2.3++/-1.1 mm. There was no statistically significant correlation between the changes in tunnel diameter and either the modified Hughston knee score, Lysholm knee score, or the joint laxity measured by a KT-2000 arthrometer, Lachman test. There were no correlations between the mild proximal migration of the tibial interference screw and the clinical results. Conclusively, the tunnel enlargement and mild proximal migration of the interference screw did not appear to affect the functional outcome adversely. It needs longer follow up for the evaluation of etiology and natural history of this tunnel enlargement.
Anterior Cruciate Ligament Reconstruction ; Autografts ; Femur ; Follow-Up Studies ; Humans ; Joint Instability ; Knee ; Knee Joint ; Natural History ; Patellar Ligament* ; Retrospective Studies ; Tibia ; Transplants*

No abstract available.
Child* ; Humans ; Reye Syndrome*

PURPOSE: To evaluate clinical outcomes of silicone tube intubation according to the site of resistance to lacrimal duct probing in complete or partial nasolacrimal duct obstruction patients. METHODS: This study included 102 eyes of 72 patients who were diagnosed with complete or partial nasolacrimal duct obstruction and who underwent silicone tube intubation. According to the site of resistant to nasolacrimal duct probing, eyes were divided into proximal resistance (Group I), distal resistance (Group II) and both side resistance (Group III). The success rate was estimated based on functional (symptom relief) and anatomical (normalization of tear meniscus) success. RESULTS: The success rates in Group I, Group II, and Group III were 53.1%, 78.8%, and 27.0%, respectively, showing that Group II attained the highest success rate (Pearson chi-square test, p = 0.001). CONCLUSIONS: In cases of only distal resistance to lacrimal probing without dacryocystography, silicone tube intubation should be performed with expectation of good clinical outcomes, even if complete nasolacrimal obstruction was suspected on syringing.
Humans ; Intubation* ; Nasolacrimal Duct ; Silicones*

PURPOSE: To evaluate clinical outcomes of silicone tube intubation according to the site of resistance to lacrimal duct probing in complete or partial nasolacrimal duct obstruction patients. METHODS: This study included 102 eyes of 72 patients who were diagnosed with complete or partial nasolacrimal duct obstruction and who underwent silicone tube intubation. According to the site of resistant to nasolacrimal duct probing, eyes were divided into proximal resistance (Group I), distal resistance (Group II) and both side resistance (Group III). The success rate was estimated based on functional (symptom relief) and anatomical (normalization of tear meniscus) success. RESULTS: The success rates in Group I, Group II, and Group III were 53.1%, 78.8%, and 27.0%, respectively, showing that Group II attained the highest success rate (Pearson chi-square test, p = 0.001). CONCLUSIONS: In cases of only distal resistance to lacrimal probing without dacryocystography, silicone tube intubation should be performed with expectation of good clinical outcomes, even if complete nasolacrimal obstruction was suspected on syringing.
Humans ; Intubation* ; Nasolacrimal Duct ; Silicones*

Laryngeal diseases have a significant impact on quality of life and often require timely and accurate diagnosis for effective management. Conventional methods of diagnosis, such as manual inspection of laryngoscopic images, have limitations in terms of accuracy and efficiency. The integration of artificial intelligence (AI) and machine learning techniques in laryngoscopic image analysis has emerged as a promising approach to enhance diagnostic accuracy, streamline workflow, and improve patient outcomes. This review paper provides an in-depth analysis of the recent advancements in AI-driven laryngoscopic image analysis for the diagnosis of laryngeal diseases, also covering methodologies, challenges, and future prospects.

The rapid progress in artificial intelligence (AI) technologies has made a significant impact on medical image analysis, particularly in cancer diagnosis. The integration of AI algorithms into histopathological image analysis holds promise for enhancing precision and efficiency in cancer diagnosis. This review provides an in-depth analysis of recent developments in AI methodologies, including deep learning approaches applied to the interpretation of histopathological images specific to head and neck cancer (HNC). Furthermore, it discusses the challenges and opportunities associated with the implementation of AI in this domain, such as data variability, interpretability, and clinical integration. Through this overview of current research findings, we aim to offer valuable insights on the state-of-the-art AI technologies, their potential impact on clinical practice, and future directions for advancing the field of HNC histopathological image analysis.

The rapid progress in artificial intelligence (AI) technologies has made a significant impact on medical image analysis, particularly in cancer diagnosis. The integration of AI algorithms into histopathological image analysis holds promise for enhancing precision and efficiency in cancer diagnosis. This review provides an in-depth analysis of recent developments in AI methodologies, including deep learning approaches applied to the interpretation of histopathological images specific to head and neck cancer (HNC). Furthermore, it discusses the challenges and opportunities associated with the implementation of AI in this domain, such as data variability, interpretability, and clinical integration. Through this overview of current research findings, we aim to offer valuable insights on the state-of-the-art AI technologies, their potential impact on clinical practice, and future directions for advancing the field of HNC histopathological image analysis.

The rapid progress in artificial intelligence (AI) technologies has made a significant impact on medical image analysis, particularly in cancer diagnosis. The integration of AI algorithms into histopathological image analysis holds promise for enhancing precision and efficiency in cancer diagnosis. This review provides an in-depth analysis of recent developments in AI methodologies, including deep learning approaches applied to the interpretation of histopathological images specific to head and neck cancer (HNC). Furthermore, it discusses the challenges and opportunities associated with the implementation of AI in this domain, such as data variability, interpretability, and clinical integration. Through this overview of current research findings, we aim to offer valuable insights on the state-of-the-art AI technologies, their potential impact on clinical practice, and future directions for advancing the field of HNC histopathological image analysis.

The rapid progress in artificial intelligence (AI) technologies has made a significant impact on medical image analysis, particularly in cancer diagnosis. The integration of AI algorithms into histopathological image analysis holds promise for enhancing precision and efficiency in cancer diagnosis. This review provides an in-depth analysis of recent developments in AI methodologies, including deep learning approaches applied to the interpretation of histopathological images specific to head and neck cancer (HNC). Furthermore, it discusses the challenges and opportunities associated with the implementation of AI in this domain, such as data variability, interpretability, and clinical integration. Through this overview of current research findings, we aim to offer valuable insights on the state-of-the-art AI technologies, their potential impact on clinical practice, and future directions for advancing the field of HNC histopathological image analysis.

The rapid progress in artificial intelligence (AI) technologies has made a significant impact on medical image analysis, particularly in cancer diagnosis. The integration of AI algorithms into histopathological image analysis holds promise for enhancing precision and efficiency in cancer diagnosis. This review provides an in-depth analysis of recent developments in AI methodologies, including deep learning approaches applied to the interpretation of histopathological images specific to head and neck cancer (HNC). Furthermore, it discusses the challenges and opportunities associated with the implementation of AI in this domain, such as data variability, interpretability, and clinical integration. Through this overview of current research findings, we aim to offer valuable insights on the state-of-the-art AI technologies, their potential impact on clinical practice, and future directions for advancing the field of HNC histopathological image analysis.