Smartphone applications for the triage of skin lesions using machine learning: time to integrate the clinical information?

2020 ◽  
Vol 34 (8) ◽  
Author(s):  
P. Acharya ◽  
M. Mathur
Keyword(s):  
Machine Learning ◽  
Clinical Information ◽  
Skin Lesions ◽  
Smartphone Applications ◽  
Learning Time

scholarly journals Predicting the Appearance of Hypotension During Hemodialysis Sessions Using Machine Learning Classifiers

2021 ◽  
Vol 18 (5) ◽  
pp. 2364
Author(s):  
Juan A. Gómez-Pulido ◽  
José M. Gómez-Pulido ◽  
Diego Rodríguez-Puyol ◽  
María-Luz Polo-Luque ◽  
Miguel Vargas-Lombardo
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Chronic Renal Disease ◽  
Clinical Information ◽  
Healthcare Personnel ◽  
Dialysis Session ◽  
Clinical Parameters ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Gender And Age

A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

scholarly journals Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma

2018 ◽  
Author(s):  
Naomi Chuchu ◽  
Yemisi Takwoingi ◽  
Jacqueline Dinnes ◽  
Rubeta N Matin ◽  
Oliver Bassett ◽  
...  
Keyword(s):  
Skin Lesions ◽  
Smartphone Applications

DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection

2020 ◽  
Vol 127 ◽  
pp. 104666 ◽  
Author(s):  
Santiago Belda ◽  
Luca Pipia ◽  
Pablo Morcillo-Pallarés ◽  
Juan Pablo Rivera-Caicedo ◽  
Eatidal Amin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Vegetation Phenology ◽  
Gap Filling ◽  
Learning Time

Malodorous Skin Lesions in a Diabetic Patient: Infectious Versus Neoplastic

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S113-S114
Author(s):  
Ismail Elbaz Younes ◽  
Julia Rewerska ◽  
Victoria Alagiozian-Angelova
Keyword(s):  
T Cell ◽  
Cell Lymphoma ◽  
Clinical Information ◽  
Skin Lesions ◽  
T Cell Lymphoma ◽  
Cytotoxic T Cell ◽  
Lymphoid Tissues ◽  
Rare Entity ◽  
Lymphomatoid Papulosis ◽  
Type D

Abstract Primary cutaneous CD8+ aggressive epidermotropic cytotoxic T-cell lymphoma is a rare entity accounting for <1% of all cutaneous T-cell lymphomas. Almost all patients present with generalized skin lesions. This type of lymphoma has an extremely aggressive course with a median survival of 12 months. It tends to spread to other visceral sites, but lymph nodes are usually spared. We describe a case of a 59-year-old male with multiple necrotic malodours ulcers for several months. The first lesion was on his left thigh, followed by another lesion on his right chest and right eyelid. Medical history revealed newly diagnosed diabetes mellitus. The patient received antibiotics, presumptively for infectious etiology of the skin lesion, with no improvement. The right thigh lesion was excised and histomorphologic examination revealed a deep dermal proliferation of large-sized pleomorphic cells with marked pagetoid epidermotropism and skin ulceration. The adnexal skin structures were invaded by the lesion. The lesional cells were immunoreactive for CD3, CD7, CD8, and granzyme B; they were negative for CD4, CD5, CD56, and CD30. The immunophenotype confirms the entity that we have at hand in addition to the similar clinical picture that the patient presented with. This disease usually shows clonal TR gene rearrangements; nonetheless, no specific mutational aberration has been described. Our patient received chemotherapy; however, new lesions continued to erupt and he opted to proceed with palliative care. Clinical information is needed to give this diagnosis as it may look identical to a variant of lymphomatoid papulosis (type D), CD8-positive cutaneous T-cell lymphoma. We present this case due to the importance of clinical pathologic coloration to prevent misdiagnosis with mimickers as the ones pointed out earlier, and it is a provisional rare entity in the 2018 WHO classification of Tumors of Haematopoietic and Lymphoid Tissues.

scholarly journals Technologies for Complex Intelligent Clinical Data Analysis

2016 ◽  
Vol 71 (2) ◽  
pp. 160-171 ◽  
Author(s):  
A. A. Baranov ◽  
L. S. Namazova-Baranova ◽  
I. V. Smirnov ◽  
D. A. Devyatkin ◽  
A. O. Shelmanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Chronic Diseases ◽  
Clinical Information ◽  
Structured Data ◽  
Unstructured Data ◽  
Intelligent Analysis ◽  
Pediatric Center

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient’s features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented.Background: Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality.Aims: the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center.Materials and methods: Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as «negation» (indicates that the disease is absent), «no patient» (indicates that the disease refers to the patient’s family member, but not to the patient), «severity of illness», «disease course», «body region to which the disease refers». Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the method for determining the most informative patients’ features are also proposed.Results: Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records of patients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases.Conclusions: The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare. 

scholarly journals A novel total body digital photography smartphone application designed to detect and monitor skin lesions: a pilot study

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Nicholas Guido ◽  
Erika L. Hagstrom ◽  
Erin Ibler ◽  
Chantelle Carneiro ◽  
Kelsey A. Orrell ◽  
...  
Keyword(s):  
Decision Making ◽  
High Sensitivity ◽  
Smartphone Application ◽  
Skin Lesions ◽  
Smartphone Applications ◽  
Self Monitoring ◽  
Pigmented Lesions ◽  
Naturally Occurring ◽  
Total Body ◽  
New Skin

<p class="Body">Although some smartphone applications are designed for total body photography (TBP), few offer the specificity that enables self- as well as dermatologist-, detection of new lesions, or change in lesion color or in size as little as 1mm, on an ongoing basis. The aim of this study is to assess the sensitivity of a novel TBP application in the detection of changes to color and size of simulated skin lesions. Twenty-five subjects underwent one study visit. After baseline photography, new artificial markings were made or naturally occurring pigmented lesions located in any anatomical region were enhanced/enlarged, and a second matching set of photographs was then taken. From all 25 subjects, a total of 262 skin markings were evaluable. Of these, 241 (92%) were detected by the app, which resulted in an overall sensitivity of 92%. The high sensitivity establishes the app as capable of providing reliable self-TBP that allows detection and monitoring of new skin lesions or change in both size and color. This method greatly enhances the ability to accomplish ongoing self-monitoring and yet provides quality informing images to the dermatologist to assist in decision-making with the patient. </p>

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