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General General

Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching.

In Current opinion in organ transplantation ; h5-index 32.0

PURPOSE OF REVIEW : Classifiers based on artificial intelligence can be useful to solve decision problems related to the inclusion or removal of possible liver transplant candidates, and assisting in the heterogeneous field of donor-recipient (D-R) matching.

RECENT FINDINGS : Artificial intelligence models can show a great advantage by being able to handle a multitude of variables, be objective and help in cases of similar probabilities. In the field of liver transplantation, the most commonly used classifiers have been artificial neural networks (ANNs) and random forest classifiers. ANNs are excellent tools for finding patterns which are far too complex for a clinician and are capable of generating near-perfect predictions on the data on which they are fit, yielding excellent prediction capabilities reaching 95% for 3 months graft survival. On the other hand, RF can overcome ANNs in some of their limitations, mainly because of the lack of information on the variables they provide. Random forest algorithms may allow for improved confidence with the use of marginal organs and better outcome after transplantation.

SUMMARY : ANNs and random forest can handle a multitude of structured and unstructured parameters, and establish non explicit relationships among risk factors of clinical relevance.

Briceno Javier, Ayllón María Dolores, Ciria Rubén


oncology Oncology

Artificial intelligence and organ transplantation: challenges and expectations.

In Current opinion in organ transplantation ; h5-index 32.0

PURPOSE OF REVIEW : Classifiers based on artificial intelligence have emerged in all areas of medicine. Although very subtle, many decisions in organ transplantation can now be addressed in a more concisely manner with the support of these classifiers.

RECENT FINDINGS : Any aspect of organ transplantation (image processing, prediction of results, diagnostic proposals, therapeutic algorithms or precision treatments) consists of a set of input variables and a set of output variables. Artificial intelligence classifiers differ in the way they establish relationships between the input variables, how they select the data groups to train patterns and how they are able to predict the possible options of the output variables. There are hundreds of classifiers to achieve this goal. The most appropriate classifiers to address the different aspects of organ transplantation are Artificial Neural Networks, Decision Tree classifiers, Random Forest, and Naïve Bayes classification models. There are hundreds of examples of the usefulness of artificial intelligence in organ transplantation, especially in image processing, organ allocation, D-R matching, precision pathology, real-time immunosuppression, transplant oncology, and predictive analysis.

SUMMARY : In the coming years, clinical transplant experts will increasingly use Deep Learning-based models to support their decisions, specially in those cases where subjectivity is common.

Briceno Javier


oncology Oncology

Artificial intelligence in transplantation (machine-learning classifiers and transplant oncology).

In Current opinion in organ transplantation ; h5-index 32.0

PURPOSE OF REVIEW : To highlight recent efforts in the development and implementation of machine learning in transplant oncology - a field that uses liver transplantation for the treatment of hepatobiliary malignancies - and particularly in hepatocellular carcinoma, the most commonly treated diagnosis in transplant oncology.

RECENT FINDINGS : The development of machine learning has occurred within three domains related to hepatocellular carcinoma: identification of key clinicopathological variables, genomics, and image processing.

SUMMARY : Machine-learning classifiers can be effectively applied for more accurate clinical prediction and handling of data, such as genetics and imaging in transplant oncology. This has allowed for the identification of factors that most significantly influence recurrence and survival in disease, such as hepatocellular carcinoma, and thus help in prognosticating patients who may benefit from a liver transplant. Although progress has been made in using these methods to analyse clinicopathological information, genomic profiles, and image processed data (both histopathological and radiomic), future progress relies on integrating data across these domains.

Ivanics Tommy, Patel Madhukar S, Erdman Lauren, Sapisochin Gonzalo


Ophthalmology Ophthalmology

Artificial intelligence in cornea, refractive, and cataract surgery.

In Current opinion in ophthalmology

PURPOSE OF REVIEW : The subject of artificial intelligence has recently been responsible for the advancement of many industries including aspects of medicine and many of its subspecialties. Within ophthalmology, artificial intelligence technology has found ways of improving the diagnostic and therapeutic processes in cornea, glaucoma, retina, and cataract surgery. As demands on the modern ophthalmologist grow, artificial intelligence can be utilized to help address increased demands of modern medicine and ophthalmology by adding to the physician's clinical and surgical acumen. The purpose of this review is to highlight the integration of artificial intelligence into ophthalmology in recent years in the areas of cornea, refractive, and cataract surgery.

RECENT FINDINGS : Within the realms of cornea, refractive, and cataract surgery, artificial intelligence has played a major role in identifying ways of improving diagnostic detection. In keratoconus, artificial intelligence algorithms may help with the early detection of keratoconus and other ectatic disorders. In cataract surgery, artificial intelligence may help improve the performance of intraocular lens (IOL) calculation formulas. Further, with its potential integration into automated refraction devices, artificial intelligence can help provide an improved framework for IOL formula optimization that is more accurate and customized to a specific cataract surgeon.

SUMMARY : The future of artificial intelligence in ophthalmology is a promising prospect. With continued advancement of mathematical and computational algorithms, corneal disease processes can be diagnosed sooner and IOL calculations can be made more accurate.

Siddiqui Aazim A, Ladas John G, Lee Jimmy K


General General

A fully automated deep learning pipeline for high-throughput colony segmentation and classification.

In Biology open

Adenine auxotrophy is a commonly used non-selective genetic marker in yeast research. It allows investigators to easily visualize and quantify various genetic and epigenetic events by simply reading out colony color. However, manual counting of large numbers of colonies is extremely time-consuming, difficult to reproduce and possibly inaccurate. Using cutting-edge neural networks, we have developed a fully automated pipeline for colony segmentation and classification, which speeds up white/red colony quantification 100-fold over manual counting by an experienced researcher. Our approach uses readily available training data and can be smoothly integrated into existing protocols, vastly speeding up screening assays and increasing the statistical power of experiments that employ adenine auxotrophy.

Carl Sarah H, Duempelmann Lea, Shimada Yukiko, Bühler Marc


Adenine auxotrophy, Deep learning, Growth assay, Neural networks, Yeast

Radiology Radiology

Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.

In BMC cancer

BACKGROUND : Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI.

METHODS : We retrospectively included a total of 242 NPC patients who underwent regular follow-up magnetic resonance imaging (MRI) examinations, including contrast-enhanced T1-weighted and T2-weighted imaging. For each MRI sequence, four non-texture and 10,320 texture features were extracted from medial temporal lobe, gray matter, and white matter, respectively. The relief and 0.632 + bootstrap algorithms were applied for initial and subsequent feature selection, respectively. Random forest method was used to construct the prediction model. Three models, 1, 2 and 3, were developed for predicting the results of the last three follow-up MRI scans at different times before RTLI onset, respectively. The area under the curve (AUC) was used to evaluate the performance of models.

RESULTS : Of the 242 patients, 171 (70.7%) were men, and the mean age of all the patients was 48.5 ± 10.4 years. The median follow-up and latency from radiotherapy until RTLI were 46 and 41 months, respectively. In the testing cohort, models 1, 2, and 3, with 20 texture features derived from the medial temporal lobe, yielded mean AUCs of 0.830 (95% CI: 0.823-0.837), 0.773 (95% CI: 0.763-0.782), and 0.716 (95% CI: 0.699-0.733), respectively.

CONCLUSION : The three developed radiomic models can dynamically predict RTLI in advance, enabling early detection and allowing clinicians to take preventive measures to stop or slow down the deterioration of RTLI.

Zhang Bin, Lian Zhouyang, Zhong Liming, Zhang Xiao, Dong Yuhao, Chen Qiuying, Zhang Lu, Mo Xiaokai, Huang Wenhui, Yang Wei, Zhang Shuixing


Machine learning, Magnetic resonance imaging, Nasopharyngeal carcinoma, Radiation-induced temporal lobe injury, Radiomics