Deep Learning-Based Assessment of Lip Symmetry for Patients With Repaired Cleft Lip.

Objective: Post-surgical lip symmetry assessment is a key indicator of cleft repair success. Traditional methods rely on distances between anatomical landmarks, which are impractical for video analysis and overlook texture and appearance. We propose an artificial intelligence (AI) approach to automate this process, analyzing lateral lip morphology for a quantitative symmetry evaluation.

Contrastive Clustering-Based Patient Normalization to Improve Automated In Vivo Oral Cancer Diagnosis from Multispectral Autofluorescence Lifetime Images.

Multispectral autofluorescence lifetime imaging systems have recently been developed to quickly and non-invasively assess tissue properties for applications in oral cancer diagnosis. As a non-traditional imaging modality, the autofluorescence signal collected from the system cannot be directly visually assessed by a clinician and a model is needed to generate a diagnosis for each image. However, training a deep learning model from scratch on small multispectral autofluorescence datasets can fail due to inter-patient variability, poor initialization, and overfitting.

Mechanisms of axoneme and centriole elimination in Naegleria gruberi.

The early branching eukaryote Naegleria gruberi can transform transiently from an amoeboid life form lacking centrioles and flagella to a flagellate life form where these elements are present, followed by reversion to the amoeboid state. The mechanisms imparting elimination of axonemes and centrioles during this reversion process are not known. Here, we uncover that flagella primarily fold onto the cell surface and fuse within milliseconds with the plasma membrane.

Prolonged survival in women with hepatocellular carcinoma: A French observational study.

Background And Aim: Less than 25 % of hepatocellular carcinoma (HCC) occurs in women, in whom prognosis could be better. Due to the lack of date in Europe, this study aims to assess survival of patients with HCC according sex in a tertiary French liver center.

Patients And Methods: Every patient diagnosed with a first diagnosis of HCC presented at our weekly multidisciplinary tumor board between 2013 and 2017 were included.

Computer-assisted discrimination of cancerous and pre-cancerous from benign oral lesions based on multispectral autofluorescence imaging endoscopy.

Significance: Diagnosis of cancerous and pre-cancerous oral lesions at early stages is critical for the improvement of patient care, to increase survival rates and minimize the invasiveness of tumor resection surgery. Unfortunately, oral precancerous and early-stage cancerous lesions are often difficult to distinguish from oral benign lesions with the existing diagnostic tools used during standard clinical oral examination. In consequence, early diagnosis of oral cancer can be achieved in only about 30% of patients.