AIMC Topic: Humans

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Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography.

The British journal of ophthalmology
AIM: To explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography.

[Minimally invasive liver surgery].

Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen
Minimally invasive liver surgery is safe and can be performed with results practically equal to those in open surgery. There are different techniques of parenchyma dissection and hemostasis available for the safe performance of minor and major resect...

[Robot-Assisted Lung Surgery].

Zentralblatt fur Chirurgie
Anatomical lung resection is the standard treatment for patients with early-stage lung cancer. The conventional surgical techniques are thoracotomy and video-assisted thoracic surgery, but new methods have been added as technology has developed. The ...

Label-free quality control and identification of human keratinocyte stem cells by deep learning-based automated cell tracking.

Stem cells (Dayton, Ohio)
Stem cell-based products have clinical and industrial applications. Thus, there is a need to develop quality control methods to standardize stem cell manufacturing. Here, we report a deep learning-based automated cell tracking (DeepACT) technology fo...

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning.

Journal of dental research
Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to se...

Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts.

Radiology
Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose T...

Development and Validation of a Radiomics Model for Differentiating Bone Islands and Osteoblastic Bone Metastases at Abdominal CT.

Radiology
Background It is important to diagnose sclerotic bone lesions in order to determine treatment strategy. Purpose To evaluate the diagnostic performance of a CT radiomics-based machine learning model for differentiating bone islands and osteoblastic bo...

Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure.

The Journal of pathology
Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pneumopathy is characterized by a complex clinical picture and heterogeneous pathological lesions, both involving alveolar and vascular components. The severity and distribution of morpholo...

Safety-driven design of machine learning for sepsis treatment.

Journal of biomedical informatics
Machine learning (ML) has the potential to bring significant clinical benefits. However, there are patient safety challenges in introducing ML in complex healthcare settings and in assuring the technology to the satisfaction of the different regulato...