Diverticular Bleeding: A Clinical Image

Acquired adult flatfoot is a three-dimensional deformation, which consists of hindfoot valgus, collapse of the longitudinal arch of the foot and adduction of the forefoot. The aim of the work is to present problems related to etiology, biomechanics, clinical diagnostics and treatment principles of acquired flatfoot. The most common cause in adults is the dysfunction of the tibialis posterior muscle, leading to the lack of blocking of the transverse tarsal joint during heel elevation. Loading the unblocked joints consequently leads to ligament failure. The clinical image is dominated by pain in the foot and tibiotarsal joint. The physical examination of the flat feet consists of: inspection, palpation, motion range assessment and dynamic force assessment. The comparable attention should be paid to the height of the foot arch, the occurrence of “too many toes” sign, evaluate the heel- rise test and correction of the flatfoot, exclude Achilles tendon contracture. The diagnosis also uses imaging tests. In elastic deformations with symptoms of posterior tibial tendonitis, non-steroidal anti-inflammatory drugs, short-term immobilization, orthotics stabilizing the medial arch of the foot are used. In rehabilitation, active exercises of the shin muscles and the feet, especially the eccentric exercises of the posterior tibial muscle, are intentional. The physiotherapy and balneotherapy treatments, in particular hydrotherapy, electrotherapy and laser therapy, are used as a support. In advanced lesions, surgical treatment may be necessary, including plastic surgery of soft tissues, tendons, as well as osteotomy procedures.

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AI-Provided Instant Differential Diagnosis of Pemphigus Vulgaris and Bullous Pemphigoid: Qualitative Study (Preprint)

10.2196/preprints.24845 ◽

2020 ◽

Author(s):

Xiaoyu He ◽

Juan Su ◽

Guangyu Wang ◽

Kang Zhang ◽

Navarini Alexander ◽

...

Keyword(s):

Artificial Intelligence ◽

Early Diagnosis ◽

Image Classification ◽

Bullous Pemphigoid ◽

Skin Diseases ◽

Pemphigus Vulgaris ◽

Classification Model ◽

Clinical Image ◽

Clinical Images ◽

Multimodal Classification

BACKGROUND Pemphigus vulgaris (PV) and bullous pemphigoid (BP) are two rare but severe inflammatory dermatoses. Due to the regional lack of trained dermatologists, many patients with these two diseases are misdiagnosed and therefore incorrectly treated. An artificial intelligence diagnosis framework would be highly adaptable for the early diagnosis of these two diseases. OBJECTIVE Design and evaluate an artificial intelligence diagnosis framework for PV and BP. METHODS The work was conducted on a dermatological dataset consisting of 17,735 clinical images and 346 patient metadata of bullous dermatoses. A two-stage diagnosis framework was designed, where the first stage trained a clinical image classification model to classify bullous dermatoses from five common skin diseases and normal skin and the second stage developed a multimodal classification model of clinical images and patient metadata to further differentiate PV and BP. RESULTS The clinical image classification model and the multimodal classification model achieved an area under the receiver operating characteristic curve (AUROC) of 0.998 and 0.942, respectively. On the independent test set of 20 PV and 20 BP cases, our multimodal classification model (sensitivity: 0.85, specificity: 0.95) performed better than the average of 27 junior dermatologists (sensitivity: 0.68, specificity: 0.78) and comparable to the average of 69 senior dermatologists (sensitivity: 0.80, specificity: 0.87). CONCLUSIONS Our diagnosis framework based on clinical images and patient metadata achieved expert-level identification of PV and BP, and is potential to be an effective tool for dermatologists in remote areas in the early diagnosis of these two diseases.

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An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images

Contrast Media & Molecular Imaging ◽

10.1155/2019/5982834 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Andrea Duggento ◽

Marco Aiello ◽

Carlo Cavaliere ◽

Giuseppe L. Cascella ◽

Davide Cascella ◽

...

Keyword(s):

Breast Cancer ◽

Network Architecture ◽

Ad Hoc ◽

False Negative ◽

Selection Criterion ◽

Current Status ◽

Clinical Image ◽

Imaging Data ◽

Breast Cancer Death ◽

Random Initialization

Breast cancer is one of the most common cancers in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide. In this context, recent studies showed that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long term. X-ray mammography is still the instrument of choice in breast cancer screening. In this context, the false-positive and false-negative rates commonly achieved by radiologists are extremely arduous to estimate and control although some authors have estimated figures of up to 20% of total diagnoses or more. The introduction of novel artificial intelligence (AI) technologies applied to the diagnosis and, possibly, prognosis of breast cancer could revolutionize the current status of the management of the breast cancer patient by assisting the radiologist in clinical image interpretation. Lately, a breakthrough in the AI field has been brought about by the introduction of deep learning techniques in general and of convolutional neural networks in particular. Such techniques require no a priori feature space definition from the operator and are able to achieve classification performances which can even surpass human experts. In this paper, we design and validate an ad hoc CNN architecture specialized in breast lesion classification from imaging data only. We explore a total of 260 model architectures in a train-validation-test split in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve an area under the receiver operatic characteristics curve of 0.785 (accuracy 71.19%) on the test set, demonstrating how an ad hoc random initialization architecture can and should be fine tuned to a specific problem, especially in biomedical applications.

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