Early diagnosis and personalised treatment focusing on synthetic data modelling: Novel visual learning approach in healthcare.

The early diagnosis and personalised treatment of diseases are facilitated by machine learning. The quality of data has an impact on diagnosis because medical data are usually sparse, imbalanced, and contain irrelevant attributes, resulting in suboptimal diagnosis. To address the impacts of data challenges, improve resource allocation, and achieve better health outcomes, a novel visual learning approach is proposed. This study contributes to the visual learning approach by determining whether less or more synthetic data are required to improve the quality of a dataset, such as the number of observations and features, according to the intended personalised treatment and early diagnosis. In addition, numerous visualisation experiments are conducted, including using statistical characteristics, cumulative sums, histograms, correlation matrix, root mean square error, and principal component analysis in order to visualise both original and synthetic data to address the data challenges. Real medical datasets for cancer, heart disease, diabetes, cryotherapy and immunotherapy are selected as case studies. As a benchmark and point of classification comparison in terms of such as accuracy, sensitivity, and specificity, several models are implemented such as k-Nearest Neighbours and Random Forest. To simulate algorithm implementation and data, Generative Adversarial Network is used to create and manipulate synthetic data, whilst, Random Forest is implemented to classify the data. An amendable and adaptable system is constructed by combining Generative Adversarial Network and Random Forest models. The system model presents working steps, overview and flowchart. Experiments reveal that the majority of data-enhancement scenarios allow for the application of visual learning in the first stage of data analysis as a novel approach. To achieve meaningful adaptable synergy between appropriate quality data and optimal classification performance while maintaining statistical characteristics, visual learning provides researchers and practitioners with practical human-in-the-loop machine learning visualisation tools. Prior to implementing algorithms, the visual learning approach can be used to actualise early, and personalised diagnosis. For the immunotherapy data, the Random Forest performed best with precision, recall, f-measure, accuracy, sensitivity, and specificity of 81%, 82%, 81%, 88%, 95%, and 60%, as opposed to 91%, 96%, 93%, 93%, 96%, and 73% for synthetic data, respectively. Future studies might examine the optimal strategies to balance the quantity and quality of medical data.

Arthroscopic transtendon repair of partial articular-sided supraspinatus tendon avulsion.

PURPOSE: To evaluate the outcomes of arthroscopic transtendon repair of partial articular-sided supraspinatus tendon avulsion (PASTA) in a large study group. METHODS: A retrospective review of prospectively collected data was conducted. One hundred and eighteen patients with PASTA lesion (grade A2-A3) who underwent arthroscopic transtendon repair were identified, of which 110 were eligible for the study. Ten patients were lost at final follow-up leaving a study group of 100 patients (52 male, 48 female). The average follow-up was 37 months (minimum 24 months, range 24-50, median 40). Mean age at the time of surgery was 50.4 (range 17-71, median 45). Patients were assessed before surgery and at 24-month follow-up, using the Simple Shoulder Test (SST), UCLA shoulder rating scale and the visual analogue scale (VAS). ROM was measured bilaterally and was evaluated before surgery, at 3-, 6- and 24-month follow-up. The satisfaction rate was calculated. Data were analysed using a paired Student's t test with 95 % confidence interval (significance p < 0.05). RESULTS: Significant improvement in UCLA, SST and VAS score was observed. Fifty-four cases were rated excellent, 42 good, 2 fair, 2 poor according to the UCLA score. No significant differences in ROM were noted compared to the contra-lateral side (p < 0.001) at the 24-month follow-up. Eighteen patients presented with a stiff shoulder at the 3-month follow-up, but they recovered full ROM by the 6-month follow-up evaluation. CONCLUSIONS: The arthroscopic transtendon repair of partial articular-sided rotator cuff tears is an effective procedure that leads to significant improvement in pain and shoulder function, with high patients' satisfaction rate, while the complication rate is low. This study demonstrated the effectiveness and safety of this technique in a large homogeneous study group. LEVEL OF EVIDENCE: IV.