Machine Learning for Health: Algorithm Auditing & Quality Control

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing

Optimal management of the newborn with an anorectal malformation and evaluation of their continence potential

Anorectal malformations affect 1 in 3000\u20135000 children, with varied incidences dependent upon geographical location. Accurate assessment, and subsequent targeted management in the newborn is critical to reducing potential morbidity and mortality. We have focused in this review upon the management of newborns with anorectal malformations, and the evaluation of the potential for long-term fecal continence

The Use of an Inanimate Simulation Model for the Correction of an Anorectal Malformation in the Training of Colorectal Pediatric Surgery

INTRODUCTION:  An anorectal malformation (ARM) is a congenital malformation that requires surgical correction. To acquire the skills needed to perform this complex procedure, an affordable simulation model has previously been developed and validated. The aim of this study is to evaluate the suitability of this ARM model (with perineal fistula) for training in hands-on workshops. MATERIALS AND METHODS:  The ARM model consists of a wooden casing with disposable perineal body. Participants in several international pediatric colorectal hands-on workshops in 2019 and 2020 were asked to participate. They were divided in a target group and an experienced group based on experience. All practiced the posterior sagittal anorectoplasty procedure on the model with multimodality guidance. Subsequently, statements on the suitability of the model for use during hands-on workshops were scored on a 5-point Likert scale. RESULTS:  A total of 80 participants were included (43 surgical specialists, 13 pediatric surgery fellows, and 25 residents). Nearly, all statements scored at least a mean of >4.0, all scored significantly better than a neutral opinion. The target group (n = 58) scored higher compared with the experienced group (n = 22) on "transferability of the skills to the clinical setting" (means 4.4 vs. 4.0, p = 0.038); however, the "suitability as a replacement for an animal model" scored significantly lower (means 3.6 vs. 3.9, p = 0.049). No other differences were found. CONCLUSION:  This affordable ARM model was regarded a suitable model for training during preclinical hands-on workshops and could be used for the specified steps of the procedure

Validation of a Newly Developed Competency Assessment Tool for the Posterior Sagittal Anorectoplasty

INTRODUCTION:  The correction of an anorectal malformation (ARM) is complex and relatively infrequent. Simulation training and subsequent assessment may result in better clinical outcomes. Assessment can be done using a competency assessment tool (CAT). This study aims to develop and validate a CAT for the posterior sagittal anorectoplasty (PSARP) on a simulation model. MATERIALS AND METHODS:  The CAT-PSARP was developed after consultation with experts in the field. The PSARP was divided into five steps, while tissue and instrument handling were scored separately. Participants of pediatric colorectal hands-on courses in 2019 and 2020 were asked to participate. They performed one PSARP procedure on an ARM simulation model, while being assessed by two objective observers using the CAT-PSARP. RESULTS:  A total of 82 participants were enrolled. A fair interobserver agreement was found for general skills (intraclass correlation coefficient [ICC] = 0.524, p < 0.001), a good agreement for specific skills (ICC = 0.646, p < 0.001), and overall performance (ICC = 0.669, p < 0.001). The experienced group scored higher on all steps (p < 0.001), except for "anoplasty (p = 0.540)," compared with an inexperienced group. CONCLUSION:  The CAT-PSARP is a suitable objective assessment tool for the overall performance of the included steps of the PSARP for repair of an ARM on a simulation model