Evaluation of hair structural abnormalities in children with different neurological diseases.

BACKGROUND: Hair microscopy is a fast and effortless diagnostic method for many diseases affecting hair in daily practice. Many diseases can present with hair shaft disorders in pediatric neurology practice. METHODS: Children with pathological hair findings were included in our study. Microscopic evaluation of the hair was performed under light microscopy. The clinical findings, pathological hair shaft findings, laboratory tests, and final diagnosis of the patients were evaluated. RESULTS: In our study, 16 patients with rare pathological hair findings were identified. Of these 16 patients, nine were diagnosed with giant axonal neuropathy, three with Griscelli syndrome, two with Menkes disease, and two with autosomal recessive woolly hair disease. In hair inspection, curly and tangled hair in patients with giant axonal neuropathy; silvery blond hair in patients with Griscelli syndrome; sparse, coarse, and light-colored hair in patients with Menkes disease; and hypotrichosis in patients with autosomal recessive woolly hair were remarkable findings. Dystrophic hair was detected in most of the patients on light microscopy. In addition, signs of trichorrhexis nodosa, tricoptylosis, and pili torti were found. In particular, pigment deposition in the hair shaft of two patients diagnosed with Griscelli syndrome and pili torti findings in two patients with Menkes disease were the most important findings suggestingthe diagnosis. CONCLUSIONS: Detection of hair findings in the physical examination and performing light microscopic evaluation facilitates the diagnosis of rare diseases accompanied by hair findings. A hair examination should be performed as a part of physical and neurological examinationson eachpatient regardless of thecomplaint.

The effect of an artificial intelligence algorithm on chest X-ray interpretation of radiology residents.

OBJECTIVE: Chest X-rays are the most commonly performed diagnostic examinations. An artificial intelligence (AI) system that evaluates the images fast and accurately help reducing workflow and management of the patients. An automated assistant may reduce the time of interpretation in daily practice. We aim to investigate whether radiology residents consider the recommendations of an AI system for their final decisions, and to assess the diagnostic performances of the residents and the AI system. METHODS: Posteroanterior (PA) chest X-rays with confirmed diagnosis were evaluated by 10 radiology residents. After interpretation, the residents checked the evaluations of the AI Algorithm and made their final decisions. Diagnostic performances of the residents without AI and after checking the AI results were compared. RESULTS: Residents' diagnostic performance for all radiological findings had a mean sensitivity of 37.9% (vs 39.8% with AI support), a mean specificity of 93.9% (vs 93.9% with AI support). The residents obtained a mean AUC of 0.660 vs 0.669 with AI support. The AI algorithm diagnostic accuracy, measured by the overall mean AUC, was 0.789. No significant difference was detected between decisions taken with and without the support of AI. CONCLUSION: Although, the AI algorithm diagnostic accuracy were higher than the residents, the radiology residents did not change their final decisions after reviewing AI recommendations. In order to benefit from these tools, the recommendations of the AI system must be more precise to the user. ADVANCES IN KNOWLEDGE: This research provides information about the willingness or resistance of radiologists to work with AI technologies via diagnostic performance tests. It also shows the diagnostic performance of an existing AI algorithm, determined by real-life data.

Artificial Neural Network Approach in Laboratory Test Reporting: Learning Algorithms.

OBJECTIVES: In the field of laboratory medicine, minimizing errors and establishing standardization is only possible by predefined processes. The aim of this study was to build an experimental decision algorithm model open to improvement that would efficiently and rapidly evaluate the results of biochemical tests with critical values by evaluating multiple factors concurrently. METHODS: The experimental model was built by Weka software (Weka, Waikato, New Zealand) based on the artificial neural network method. Data were received from Dokuz Eylül University Central Laboratory. "Training sets" were developed for our experimental model to teach the evaluation criteria. After training the system, "test sets" developed for different conditions were used to statistically assess the validity of the model. RESULTS: After developing the decision algorithm with three iterations of training, no result was verified that was refused by the laboratory specialist. The sensitivity of the model was 91% and specificity was 100%. The estimated κ score was 0.950. CONCLUSIONS: This is the first study based on an artificial neural network to build an experimental assessment and decision algorithm model. By integrating our trained algorithm model into a laboratory information system, it may be possible to reduce employees' workload without compromising patient safety.

Algorithms for scheduling of chemotherapy plans.

Chemotherapy is used to control and cure cancer by using drugs to destroy cancer cells. Treatment schedules for chemotherapy may vary depending on the type of cancer, the goals of treatment, the type of chemotherapy and the patient's state of health. Chemotherapy is usually given in cycles of a treatment-period followed by a rest-period. An oncologist decides the choice of a particular regimen; however, modifications to drug dose and schedule are often necessary because of variabilities in the health of an individual patient. Therefore an orderly execution of chemotherapy regimens requires management, scheduling and allocation of the resources available. Chemotherapy scheduling is an optimization problem. In this paper, a two-phase approach has been adopted to deal with the problem. An adaptive negative-feedback scheduling algorithm is proposed for the first phase to control the load on the system. Two heuristics based on the 'Multiple Knapsack Problem' have been evaluated for the second phase to assign patients to specific infusion seats. The overall design has been put to test at a local chemotherapy center and has yielded good results for patient waiting times, orderly execution of chemotherapy regimen and utilization of infusion chairs.